class AuditTrail:
def __init__(self, storage, encryption_key):
self.storage = storage
self.cipher = AES256(encryption_key)
async def log_access(self, event: AuditEvent):
record = {
"timestamp": datetime.utcnow().isoformat(),
"actor": event.user_id,
"action": event.action,
"resource": event.resource_id,
"resource_type": event.resource_type,
"ip_address": event.ip_address,
"result": event.result,
"metadata": event.metadata,
}
encrypted = self.cipher.encrypt(
json.dumps(record).encode()
)
await self.storage.append(
partition=event.resource_type,
key=f"{event.timestamp}_{event.user_id}",
value=encrypted,
)
if event.action in REPORTABLE_ACTIONS:
await self.notify_compliance(record)
class AuditTrail:
def __init__(self, storage, encryption_key):
self.storage = storage
self.cipher = AES256(encryption_key)
async def log_access(self, event: AuditEvent):
record = {
"timestamp": datetime.utcnow().isoformat(),
"actor": event.user_id,
"action": event.action,
"resource": event.resource_id,
"resource_type": event.resource_type,
"ip_address": event.ip_address,
"result": event.result,
"metadata": event.metadata,
}
encrypted = self.cipher.encrypt(
json.dumps(record).encode()
)
await self.storage.append(
partition=event.resource_type,
key=f"{event.timestamp}_{event.user_id}",
value=encrypted,
)
if event.action in REPORTABLE_ACTIONS:
await self.notify_compliance(record)
SELECT
tenants.id,
tenants.name,
units.address,
leases.end_date,
leases.monthly_rent,
CASE
WHEN leases.end_date - CURRENT_DATE <= 90
THEN 'urgent'
WHEN leases.end_date - CURRENT_DATE <= 180
THEN 'upcoming'
ELSE 'active'
END AS renewal_status,
tenant_scores.payment_score,
market_rates.suggested_rent
FROM tenants
JOIN leases ON tenants.id = leases.tenant_id
JOIN units ON leases.unit_id = units.id
LEFT JOIN tenant_scores
ON tenants.id = tenant_scores.tenant_id
LEFT JOIN market_rates
ON units.zip_code = market_rates.zip_code
WHERE leases.end_date <= CURRENT_DATE + 180
AND leases.status = 'active'
ORDER BY leases.end_date ASC;
SELECT
tenants.id,
tenants.name,
units.address,
leases.end_date,
leases.monthly_rent,
CASE
WHEN leases.end_date - CURRENT_DATE <= 90
THEN 'urgent'
WHEN leases.end_date - CURRENT_DATE <= 180
THEN 'upcoming'
ELSE 'active'
END AS renewal_status,
tenant_scores.payment_score,
market_rates.suggested_rent
FROM tenants
JOIN leases ON tenants.id = leases.tenant_id
JOIN units ON leases.unit_id = units.id
LEFT JOIN tenant_scores
ON tenants.id = tenant_scores.tenant_id
LEFT JOIN market_rates
ON units.zip_code = market_rates.zip_code
WHERE leases.end_date <= CURRENT_DATE + 180
AND leases.status = 'active'
ORDER BY leases.end_date ASC;
class RetrievalPipeline:
def __init__(self, embedder, vector_db, reranker):
self.embedder = embedder
self.vector_db = vector_db
self.reranker = reranker
async def retrieve(self, query, top_k=10):
embedding = await self.embedder.embed(query)
candidates = await self.vector_db.search(
vector=embedding,
top_k=top_k * 3,
filter={"status": "published"},
)
reranked = await self.reranker.rank(
query=query,
documents=[c.text for c in candidates],
top_k=top_k,
)
return [
RetrievedDoc(
text=doc.text,
score=doc.score,
source=doc.metadata["source"],
)
for doc in reranked
]
class RetrievalPipeline:
def __init__(self, embedder, vector_db, reranker):
self.embedder = embedder
self.vector_db = vector_db
self.reranker = reranker
async def retrieve(self, query, top_k=10):
embedding = await self.embedder.embed(query)
candidates = await self.vector_db.search(
vector=embedding,
top_k=top_k * 3,
filter={"status": "published"},
)
reranked = await self.reranker.rank(
query=query,
documents=[c.text for c in candidates],
top_k=top_k,
)
return [
RetrievedDoc(
text=doc.text,
score=doc.score,
source=doc.metadata["source"],
)
for doc in reranked
]
export async function POST(req: Request) {
const { messages, tools } = await req.json();
const session = await auth.verify(req);
const stream = await anthropic.messages.stream({
model: "claude-sonnet-4-20250514",
max_tokens: 4096,
system: buildSystemPrompt(session),
messages,
tools: tools ?? defaultTools,
});
const encoder = new TextEncoder();
const readable = new ReadableStream({
async start(controller) {
for await (const event of stream) {
const data = JSON.stringify(event);
controller.enqueue(
encoder.encode("data: " + data + "\n\n")
);
}
controller.close();
},
});
return new Response(readable, {
headers: {
"Content-Type": "text/event-stream",
"Cache-Control": "no-cache",
},
});
}
export async function POST(req: Request) {
const { messages, tools } = await req.json();
const session = await auth.verify(req);
const stream = await anthropic.messages.stream({
model: "claude-sonnet-4-20250514",
max_tokens: 4096,
system: buildSystemPrompt(session),
messages,
tools: tools ?? defaultTools,
});
const encoder = new TextEncoder();
const readable = new ReadableStream({
async start(controller) {
for await (const event of stream) {
const data = JSON.stringify(event);
controller.enqueue(
encoder.encode("data: " + data + "\n\n")
);
}
controller.close();
},
});
return new Response(readable, {
headers: {
"Content-Type": "text/event-stream",
"Cache-Control": "no-cache",
},
});
}
func (h *DispatchHub) Run() {
for {
select {
case driver := <-h.register:
h.drivers[driver.ID] = driver
h.broadcastDriverCount()
case driver := <-h.unregister:
delete(h.drivers, driver.ID)
close(driver.Send)
h.broadcastDriverCount()
case update := <-h.locationUpdates:
h.drivers[update.DriverID].Location = update.Position
h.grid.Update(update.DriverID, update.Position)
nearby := h.grid.FindNearby(
update.Position, 5*time.Minute,
)
h.pendingRequests.TryMatch(nearby)
case req := <-h.tripRequests:
candidates := h.grid.FindNearby(
req.Pickup, 10*time.Minute,
)
if best := h.scorer.Rank(candidates, req); best != nil {
h.assign(best, req)
} else {
h.pendingRequests.Enqueue(req)
}
}
}
}
func (h *DispatchHub) Run() {
for {
select {
case driver := <-h.register:
h.drivers[driver.ID] = driver
h.broadcastDriverCount()
case driver := <-h.unregister:
delete(h.drivers, driver.ID)
close(driver.Send)
h.broadcastDriverCount()
case update := <-h.locationUpdates:
h.drivers[update.DriverID].Location = update.Position
h.grid.Update(update.DriverID, update.Position)
nearby := h.grid.FindNearby(
update.Position, 5*time.Minute,
)
h.pendingRequests.TryMatch(nearby)
case req := <-h.tripRequests:
candidates := h.grid.FindNearby(
req.Pickup, 10*time.Minute,
)
if best := h.scorer.Rank(candidates, req); best != nil {
h.assign(best, req)
} else {
h.pendingRequests.Enqueue(req)
}
}
}
}
class PatientDataPipeline:
def __init__(self, ehr_client, fhir_endpoint):
self.ehr = ehr_client
self.fhir = fhir_endpoint
self.audit = AuditLogger("hipaa")
async def sync_encounters(self, patient_id):
self.audit.log("access", patient_id)
raw = await self.ehr.get_encounters(patient_id)
normalized = [
self.to_fhir_encounter(e) for e in raw
]
await self.fhir.batch_upsert(normalized)
return normalized
def to_fhir_encounter(self, encounter):
return {
"resourceType": "Encounter",
"status": encounter["status"],
"class": encounter["visit_type"],
"subject": encounter["patient_ref"],
"period": {
"start": encounter["admitted_at"],
"end": encounter["discharged_at"],
},
}
class PatientDataPipeline:
def __init__(self, ehr_client, fhir_endpoint):
self.ehr = ehr_client
self.fhir = fhir_endpoint
self.audit = AuditLogger("hipaa")
async def sync_encounters(self, patient_id):
self.audit.log("access", patient_id)
raw = await self.ehr.get_encounters(patient_id)
normalized = [
self.to_fhir_encounter(e) for e in raw
]
await self.fhir.batch_upsert(normalized)
return normalized
def to_fhir_encounter(self, encounter):
return {
"resourceType": "Encounter",
"status": encounter["status"],
"class": encounter["visit_type"],
"subject": encounter["patient_ref"],
"period": {
"start": encounter["admitted_at"],
"end": encounter["discharged_at"],
},
}
use std::sync::Arc;
use tokio::sync::Mutex;
struct ConnectionPool {
connections: Arc<Mutex<Vec<Connection>>>,
max_size: usize,
health_check_interval: Duration,
}
impl ConnectionPool {
async fn acquire(&self) -> Result<Connection> {
let mut pool = self.connections.lock().await;
while let Some(conn) = pool.pop() {
if conn.is_healthy().await {
return Ok(conn);
}
conn.close().await;
}
self.create_connection().await
}
async fn release(&self, conn: Connection) {
let mut pool = self.connections.lock().await;
if pool.len() < self.max_size {
pool.push(conn);
} else {
conn.close().await;
}
}
async fn health_check_loop(&self) {
loop {
tokio::time::sleep(self.health_check_interval).await;
let mut pool = self.connections.lock().await;
pool.retain(|c| c.last_used.elapsed() < Duration::from_secs(300));
}
}
}
use std::sync::Arc;
use tokio::sync::Mutex;
struct ConnectionPool {
connections: Arc<Mutex<Vec<Connection>>>,
max_size: usize,
health_check_interval: Duration,
}
impl ConnectionPool {
async fn acquire(&self) -> Result<Connection> {
let mut pool = self.connections.lock().await;
while let Some(conn) = pool.pop() {
if conn.is_healthy().await {
return Ok(conn);
}
conn.close().await;
}
self.create_connection().await
}
async fn release(&self, conn: Connection) {
let mut pool = self.connections.lock().await;
if pool.len() < self.max_size {
pool.push(conn);
} else {
conn.close().await;
}
}
async fn health_check_loop(&self) {
loop {
tokio::time::sleep(self.health_check_interval).await;
let mut pool = self.connections.lock().await;
pool.retain(|c| c.last_used.elapsed() < Duration::from_secs(300));
}
}
}
fn optimize_dispatch(
drivers: &[Driver],
requests: &[TripRequest],
constraints: &DispatchConstraints,
) -> Vec<Assignment> {
let mut assignments = Vec::new();
let mut available = drivers.to_vec();
let scored: Vec<_> = requests.iter()
.flat_map(|req| {
available.iter().map(move |driver| {
let eta = calculate_eta(driver, req);
let score = score_match(driver, req, eta);
(driver.id, req.id, score, eta)
})
})
.collect();
scored.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap());
for (driver_id, req_id, score, eta) in scored {
if !available.iter().any(|d| d.id == driver_id) {
continue;
}
if eta > constraints.max_eta {
continue;
}
assignments.push(Assignment {
driver_id, req_id, score, eta,
});
available.retain(|d| d.id != driver_id);
}
assignments
}
fn optimize_dispatch(
drivers: &[Driver],
requests: &[TripRequest],
constraints: &DispatchConstraints,
) -> Vec<Assignment> {
let mut assignments = Vec::new();
let mut available = drivers.to_vec();
let scored: Vec<_> = requests.iter()
.flat_map(|req| {
available.iter().map(move |driver| {
let eta = calculate_eta(driver, req);
let score = score_match(driver, req, eta);
(driver.id, req.id, score, eta)
})
})
.collect();
scored.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap());
for (driver_id, req_id, score, eta) in scored {
if !available.iter().any(|d| d.id == driver_id) {
continue;
}
if eta > constraints.max_eta {
continue;
}
assignments.push(Assignment {
driver_id, req_id, score, eta,
});
available.retain(|d| d.id != driver_id);
}
assignments
}
async function runETL(config: ETLConfig) {
const source = createSource(config.source);
const sink = createSink(config.destination);
let processed = 0;
let errors = 0;
for await (const batch of source.stream(1000)) {
const transformed = batch
.map(record => {
try {
const clean = normalize(record);
const valid = config.schema.parse(clean);
return { ok: true, data: valid };
} catch (e) {
errors++;
deadLetter.send(record, e);
return { ok: false };
}
})
.filter(r => r.ok)
.map(r => r.data);
await sink.writeBatch(transformed);
processed += transformed.length;
metrics.gauge("etl.processed", processed);
metrics.gauge("etl.errors", errors);
}
return { processed, errors };
}
async function runETL(config: ETLConfig) {
const source = createSource(config.source);
const sink = createSink(config.destination);
let processed = 0;
let errors = 0;
for await (const batch of source.stream(1000)) {
const transformed = batch
.map(record => {
try {
const clean = normalize(record);
const valid = config.schema.parse(clean);
return { ok: true, data: valid };
} catch (e) {
errors++;
deadLetter.send(record, e);
return { ok: false };
}
})
.filter(r => r.ok)
.map(r => r.data);
await sink.writeBatch(transformed);
processed += transformed.length;
metrics.gauge("etl.processed", processed);
metrics.gauge("etl.errors", errors);
}
return { processed, errors };
}
async function runAgentWorkflow(task: Task) {
const planner = new PlannerAgent({
model: "claude-sonnet-4-20250514",
tools: [searchTool, sqlTool, emailTool],
});
const plan = await planner.decompose(task);
const results: StepResult[] = [];
for (const step of plan.steps) {
const agent = AgentPool.acquire(step.type);
const result = await agent.execute(step, {
context: results,
timeout: 30_000,
retries: 2,
});
results.push(result);
if (result.requiresApproval) {
await HumanInTheLoop.request({
step,
result,
assignee: task.owner,
});
}
}
return planner.synthesize(results);
}
async function runAgentWorkflow(task: Task) {
const planner = new PlannerAgent({
model: "claude-sonnet-4-20250514",
tools: [searchTool, sqlTool, emailTool],
});
const plan = await planner.decompose(task);
const results: StepResult[] = [];
for (const step of plan.steps) {
const agent = AgentPool.acquire(step.type);
const result = await agent.execute(step, {
context: results,
timeout: 30_000,
retries: 2,
});
results.push(result);
if (result.requiresApproval) {
await HumanInTheLoop.request({
step,
result,
assignee: task.owner,
});
}
}
return planner.synthesize(results);
}
class IntakeProcessor:
def __init__(self, ocr, classifier, crm):
self.ocr = ocr
self.classifier = classifier
self.crm = crm
async def process(self, document):
text = await self.ocr.extract(document.file)
doc_type = await self.classifier.classify(text)
if doc_type == "insurance_card":
data = await self.extract_insurance(text)
await self.crm.update_insurance(
patient_id=document.patient_id,
carrier=data["carrier"],
policy_number=data["policy"],
group_number=data["group"],
)
elif doc_type == "referral":
data = await self.extract_referral(text)
await self.crm.create_referral(
patient_id=document.patient_id,
provider=data["referring_provider"],
reason=data["reason"],
valid_until=data["expiration"],
)
await self.audit_log(document, doc_type)
return doc_type
class IntakeProcessor:
def __init__(self, ocr, classifier, crm):
self.ocr = ocr
self.classifier = classifier
self.crm = crm
async def process(self, document):
text = await self.ocr.extract(document.file)
doc_type = await self.classifier.classify(text)
if doc_type == "insurance_card":
data = await self.extract_insurance(text)
await self.crm.update_insurance(
patient_id=document.patient_id,
carrier=data["carrier"],
policy_number=data["policy"],
group_number=data["group"],
)
elif doc_type == "referral":
data = await self.extract_referral(text)
await self.crm.create_referral(
patient_id=document.patient_id,
provider=data["referring_provider"],
reason=data["reason"],
valid_until=data["expiration"],
)
await self.audit_log(document, doc_type)
return doc_type
export async function processContract(
document: Buffer,
schema: ContractSchema,
) {
const pages = await pdf.parse(document);
const chunks = chunkBySection(pages, {
maxTokens: 2000,
overlap: 200,
});
const extractions = await Promise.all(
chunks.map(chunk =>
anthropic.messages.create({
model: "claude-sonnet-4-20250514",
messages: [{
role: "user",
content: [
{ type: "text", text: EXTRACTION_PROMPT },
{ type: "text", text: chunk.text },
],
}],
max_tokens: 4096,
})
)
);
const merged = mergeExtractions(extractions);
const validated = schema.parse(merged);
await db.contracts.upsert(validated);
return validated;
}
export async function processContract(
document: Buffer,
schema: ContractSchema,
) {
const pages = await pdf.parse(document);
const chunks = chunkBySection(pages, {
maxTokens: 2000,
overlap: 200,
});
const extractions = await Promise.all(
chunks.map(chunk =>
anthropic.messages.create({
model: "claude-sonnet-4-20250514",
messages: [{
role: "user",
content: [
{ type: "text", text: EXTRACTION_PROMPT },
{ type: "text", text: chunk.text },
],
}],
max_tokens: 4096,
})
)
);
const merged = mergeExtractions(extractions);
const validated = schema.parse(merged);
await db.contracts.upsert(validated);
return validated;
}
resource "aws_ecs_service" "api" {
name = "novostra-api-prod"
cluster = aws_ecs_cluster.main.id
task_definition = aws_ecs_task_definition.api.arn
desired_count = 3
launch_type = "FARGATE"
network_configuration {
subnets = var.private_subnets
security_groups = [aws_security_group.api.id]
assign_public_ip = false
}
load_balancer {
target_group_arn = aws_lb_target_group.api.arn
container_name = "api"
container_port = 8080
}
service_registries {
registry_arn = aws_service_discovery_service.api.arn
}
deployment_circuit_breaker {
enable = true
rollback = true
}
}
resource "aws_ecs_service" "api" {
name = "novostra-api-prod"
cluster = aws_ecs_cluster.main.id
task_definition = aws_ecs_task_definition.api.arn
desired_count = 3
launch_type = "FARGATE"
network_configuration {
subnets = var.private_subnets
security_groups = [aws_security_group.api.id]
assign_public_ip = false
}
load_balancer {
target_group_arn = aws_lb_target_group.api.arn
container_name = "api"
container_port = 8080
}
service_registries {
registry_arn = aws_service_discovery_service.api.arn
}
deployment_circuit_breaker {
enable = true
rollback = true
}
}
template<typename T, size_t Dim>
class VectorIndex {
HNSWGraph<Dim> graph_;
std::unordered_map<uint64_t, Metadata> meta_;
mutable std::shared_mutex mutex_;
public:
void insert(uint64_t id,
const Vector<Dim>& vec,
Metadata meta) {
std::unique_lock lock(mutex_);
graph_.add_point(vec.data(), id);
meta_[id] = std::move(meta);
}
std::vector<SearchResult> search(
const Vector<Dim>& query,
size_t top_k,
const Filter& filter = {}) const
{
std::shared_lock lock(mutex_);
auto neighbors = graph_.search(
query.data(), top_k * 2
);
std::vector<SearchResult> results;
for (auto& [id, dist] : neighbors) {
if (filter.matches(meta_.at(id))) {
results.push_back({id, dist, meta_.at(id)});
}
if (results.size() >= top_k) break;
}
return results;
}
};
template<typename T, size_t Dim>
class VectorIndex {
HNSWGraph<Dim> graph_;
std::unordered_map<uint64_t, Metadata> meta_;
mutable std::shared_mutex mutex_;
public:
void insert(uint64_t id,
const Vector<Dim>& vec,
Metadata meta) {
std::unique_lock lock(mutex_);
graph_.add_point(vec.data(), id);
meta_[id] = std::move(meta);
}
std::vector<SearchResult> search(
const Vector<Dim>& query,
size_t top_k,
const Filter& filter = {}) const
{
std::shared_lock lock(mutex_);
auto neighbors = graph_.search(
query.data(), top_k * 2
);
std::vector<SearchResult> results;
for (auto& [id, dist] : neighbors) {
if (filter.matches(meta_.at(id))) {
results.push_back({id, dist, meta_.at(id)});
}
if (results.size() >= top_k) break;
}
return results;
}
};
async def route_maintenance_request(request):
urgency = await classifier.assess_urgency(
description=request.description,
photos=request.attachments,
)
vendor = await find_best_vendor(
category=urgency.category,
location=request.unit.address,
budget=request.property.maintenance_budget,
)
work_order = await create_work_order(
request=request,
vendor=vendor,
priority=urgency.level,
estimated_cost=vendor.estimate,
)
await notify_tenant(
tenant=request.tenant,
work_order=work_order,
eta=vendor.next_available,
)
await notify_property_manager(
manager=request.property.manager,
work_order=work_order,
)
return work_order
async def route_maintenance_request(request):
urgency = await classifier.assess_urgency(
description=request.description,
photos=request.attachments,
)
vendor = await find_best_vendor(
category=urgency.category,
location=request.unit.address,
budget=request.property.maintenance_budget,
)
work_order = await create_work_order(
request=request,
vendor=vendor,
priority=urgency.level,
estimated_cost=vendor.estimate,
)
await notify_tenant(
tenant=request.tenant,
work_order=work_order,
eta=vendor.next_available,
)
await notify_property_manager(
manager=request.property.manager,
work_order=work_order,
)
return work_order
async def evaluate_models(
test_suite: TestSuite,
models: list[ModelConfig],
) -> EvalReport:
results = {}
for model in models:
client = init_client(model)
scores = []
for case in test_suite.cases:
response = await client.generate(
prompt=case.prompt,
temperature=0,
max_tokens=case.max_tokens,
)
score = await grade_response(
response=response.text,
expected=case.expected,
rubric=case.rubric,
grader_model="claude-sonnet-4-20250514",
)
scores.append(score)
results[model.name] = {
"accuracy": mean(s.accuracy for s in scores),
"latency_p50": percentile(s.latency for s in scores, 50),
"latency_p99": percentile(s.latency for s in scores, 99),
"cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000,
}
return EvalReport(results=results, suite=test_suite)
async def evaluate_models(
test_suite: TestSuite,
models: list[ModelConfig],
) -> EvalReport:
results = {}
for model in models:
client = init_client(model)
scores = []
for case in test_suite.cases:
response = await client.generate(
prompt=case.prompt,
temperature=0,
max_tokens=case.max_tokens,
)
score = await grade_response(
response=response.text,
expected=case.expected,
rubric=case.rubric,
grader_model="claude-sonnet-4-20250514",
)
scores.append(score)
results[model.name] = {
"accuracy": mean(s.accuracy for s in scores),
"latency_p50": percentile(s.latency for s in scores, 50),
"latency_p99": percentile(s.latency for s in scores, 99),
"cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000,
}
return EvalReport(results=results, suite=test_suite)
class AuditTrail:
def __init__(self, storage, encryption_key):
self.storage = storage
self.cipher = AES256(encryption_key)
async def log_access(self, event: AuditEvent):
record = {
"timestamp": datetime.utcnow().isoformat(),
"actor": event.user_id,
"action": event.action,
"resource": event.resource_id,
"resource_type": event.resource_type,
"ip_address": event.ip_address,
"result": event.result,
"metadata": event.metadata,
}
encrypted = self.cipher.encrypt(
json.dumps(record).encode()
)
await self.storage.append(
partition=event.resource_type,
key=f"{event.timestamp}_{event.user_id}",
value=encrypted,
)
if event.action in REPORTABLE_ACTIONS:
await self.notify_compliance(record)
class AuditTrail:
def __init__(self, storage, encryption_key):
self.storage = storage
self.cipher = AES256(encryption_key)
async def log_access(self, event: AuditEvent):
record = {
"timestamp": datetime.utcnow().isoformat(),
"actor": event.user_id,
"action": event.action,
"resource": event.resource_id,
"resource_type": event.resource_type,
"ip_address": event.ip_address,
"result": event.result,
"metadata": event.metadata,
}
encrypted = self.cipher.encrypt(
json.dumps(record).encode()
)
await self.storage.append(
partition=event.resource_type,
key=f"{event.timestamp}_{event.user_id}",
value=encrypted,
)
if event.action in REPORTABLE_ACTIONS:
await self.notify_compliance(record)
SELECT
tenants.id,
tenants.name,
units.address,
leases.end_date,
leases.monthly_rent,
CASE
WHEN leases.end_date - CURRENT_DATE <= 90
THEN 'urgent'
WHEN leases.end_date - CURRENT_DATE <= 180
THEN 'upcoming'
ELSE 'active'
END AS renewal_status,
tenant_scores.payment_score,
market_rates.suggested_rent
FROM tenants
JOIN leases ON tenants.id = leases.tenant_id
JOIN units ON leases.unit_id = units.id
LEFT JOIN tenant_scores
ON tenants.id = tenant_scores.tenant_id
LEFT JOIN market_rates
ON units.zip_code = market_rates.zip_code
WHERE leases.end_date <= CURRENT_DATE + 180
AND leases.status = 'active'
ORDER BY leases.end_date ASC;
SELECT
tenants.id,
tenants.name,
units.address,
leases.end_date,
leases.monthly_rent,
CASE
WHEN leases.end_date - CURRENT_DATE <= 90
THEN 'urgent'
WHEN leases.end_date - CURRENT_DATE <= 180
THEN 'upcoming'
ELSE 'active'
END AS renewal_status,
tenant_scores.payment_score,
market_rates.suggested_rent
FROM tenants
JOIN leases ON tenants.id = leases.tenant_id
JOIN units ON leases.unit_id = units.id
LEFT JOIN tenant_scores
ON tenants.id = tenant_scores.tenant_id
LEFT JOIN market_rates
ON units.zip_code = market_rates.zip_code
WHERE leases.end_date <= CURRENT_DATE + 180
AND leases.status = 'active'
ORDER BY leases.end_date ASC;
class RetrievalPipeline:
def __init__(self, embedder, vector_db, reranker):
self.embedder = embedder
self.vector_db = vector_db
self.reranker = reranker
async def retrieve(self, query, top_k=10):
embedding = await self.embedder.embed(query)
candidates = await self.vector_db.search(
vector=embedding,
top_k=top_k * 3,
filter={"status": "published"},
)
reranked = await self.reranker.rank(
query=query,
documents=[c.text for c in candidates],
top_k=top_k,
)
return [
RetrievedDoc(
text=doc.text,
score=doc.score,
source=doc.metadata["source"],
)
for doc in reranked
]
class RetrievalPipeline:
def __init__(self, embedder, vector_db, reranker):
self.embedder = embedder
self.vector_db = vector_db
self.reranker = reranker
async def retrieve(self, query, top_k=10):
embedding = await self.embedder.embed(query)
candidates = await self.vector_db.search(
vector=embedding,
top_k=top_k * 3,
filter={"status": "published"},
)
reranked = await self.reranker.rank(
query=query,
documents=[c.text for c in candidates],
top_k=top_k,
)
return [
RetrievedDoc(
text=doc.text,
score=doc.score,
source=doc.metadata["source"],
)
for doc in reranked
]
export async function POST(req: Request) {
const { messages, tools } = await req.json();
const session = await auth.verify(req);
const stream = await anthropic.messages.stream({
model: "claude-sonnet-4-20250514",
max_tokens: 4096,
system: buildSystemPrompt(session),
messages,
tools: tools ?? defaultTools,
});
const encoder = new TextEncoder();
const readable = new ReadableStream({
async start(controller) {
for await (const event of stream) {
const data = JSON.stringify(event);
controller.enqueue(
encoder.encode("data: " + data + "\n\n")
);
}
controller.close();
},
});
return new Response(readable, {
headers: {
"Content-Type": "text/event-stream",
"Cache-Control": "no-cache",
},
});
}
export async function POST(req: Request) {
const { messages, tools } = await req.json();
const session = await auth.verify(req);
const stream = await anthropic.messages.stream({
model: "claude-sonnet-4-20250514",
max_tokens: 4096,
system: buildSystemPrompt(session),
messages,
tools: tools ?? defaultTools,
});
const encoder = new TextEncoder();
const readable = new ReadableStream({
async start(controller) {
for await (const event of stream) {
const data = JSON.stringify(event);
controller.enqueue(
encoder.encode("data: " + data + "\n\n")
);
}
controller.close();
},
});
return new Response(readable, {
headers: {
"Content-Type": "text/event-stream",
"Cache-Control": "no-cache",
},
});
}
func (h *DispatchHub) Run() {
for {
select {
case driver := <-h.register:
h.drivers[driver.ID] = driver
h.broadcastDriverCount()
case driver := <-h.unregister:
delete(h.drivers, driver.ID)
close(driver.Send)
h.broadcastDriverCount()
case update := <-h.locationUpdates:
h.drivers[update.DriverID].Location = update.Position
h.grid.Update(update.DriverID, update.Position)
nearby := h.grid.FindNearby(
update.Position, 5*time.Minute,
)
h.pendingRequests.TryMatch(nearby)
case req := <-h.tripRequests:
candidates := h.grid.FindNearby(
req.Pickup, 10*time.Minute,
)
if best := h.scorer.Rank(candidates, req); best != nil {
h.assign(best, req)
} else {
h.pendingRequests.Enqueue(req)
}
}
}
}
func (h *DispatchHub) Run() {
for {
select {
case driver := <-h.register:
h.drivers[driver.ID] = driver
h.broadcastDriverCount()
case driver := <-h.unregister:
delete(h.drivers, driver.ID)
close(driver.Send)
h.broadcastDriverCount()
case update := <-h.locationUpdates:
h.drivers[update.DriverID].Location = update.Position
h.grid.Update(update.DriverID, update.Position)
nearby := h.grid.FindNearby(
update.Position, 5*time.Minute,
)
h.pendingRequests.TryMatch(nearby)
case req := <-h.tripRequests:
candidates := h.grid.FindNearby(
req.Pickup, 10*time.Minute,
)
if best := h.scorer.Rank(candidates, req); best != nil {
h.assign(best, req)
} else {
h.pendingRequests.Enqueue(req)
}
}
}
}
class PatientDataPipeline:
def __init__(self, ehr_client, fhir_endpoint):
self.ehr = ehr_client
self.fhir = fhir_endpoint
self.audit = AuditLogger("hipaa")
async def sync_encounters(self, patient_id):
self.audit.log("access", patient_id)
raw = await self.ehr.get_encounters(patient_id)
normalized = [
self.to_fhir_encounter(e) for e in raw
]
await self.fhir.batch_upsert(normalized)
return normalized
def to_fhir_encounter(self, encounter):
return {
"resourceType": "Encounter",
"status": encounter["status"],
"class": encounter["visit_type"],
"subject": encounter["patient_ref"],
"period": {
"start": encounter["admitted_at"],
"end": encounter["discharged_at"],
},
}
class PatientDataPipeline:
def __init__(self, ehr_client, fhir_endpoint):
self.ehr = ehr_client
self.fhir = fhir_endpoint
self.audit = AuditLogger("hipaa")
async def sync_encounters(self, patient_id):
self.audit.log("access", patient_id)
raw = await self.ehr.get_encounters(patient_id)
normalized = [
self.to_fhir_encounter(e) for e in raw
]
await self.fhir.batch_upsert(normalized)
return normalized
def to_fhir_encounter(self, encounter):
return {
"resourceType": "Encounter",
"status": encounter["status"],
"class": encounter["visit_type"],
"subject": encounter["patient_ref"],
"period": {
"start": encounter["admitted_at"],
"end": encounter["discharged_at"],
},
}
use std::sync::Arc;
use tokio::sync::Mutex;
struct ConnectionPool {
connections: Arc<Mutex<Vec<Connection>>>,
max_size: usize,
health_check_interval: Duration,
}
impl ConnectionPool {
async fn acquire(&self) -> Result<Connection> {
let mut pool = self.connections.lock().await;
while let Some(conn) = pool.pop() {
if conn.is_healthy().await {
return Ok(conn);
}
conn.close().await;
}
self.create_connection().await
}
async fn release(&self, conn: Connection) {
let mut pool = self.connections.lock().await;
if pool.len() < self.max_size {
pool.push(conn);
} else {
conn.close().await;
}
}
async fn health_check_loop(&self) {
loop {
tokio::time::sleep(self.health_check_interval).await;
let mut pool = self.connections.lock().await;
pool.retain(|c| c.last_used.elapsed() < Duration::from_secs(300));
}
}
}
use std::sync::Arc;
use tokio::sync::Mutex;
struct ConnectionPool {
connections: Arc<Mutex<Vec<Connection>>>,
max_size: usize,
health_check_interval: Duration,
}
impl ConnectionPool {
async fn acquire(&self) -> Result<Connection> {
let mut pool = self.connections.lock().await;
while let Some(conn) = pool.pop() {
if conn.is_healthy().await {
return Ok(conn);
}
conn.close().await;
}
self.create_connection().await
}
async fn release(&self, conn: Connection) {
let mut pool = self.connections.lock().await;
if pool.len() < self.max_size {
pool.push(conn);
} else {
conn.close().await;
}
}
async fn health_check_loop(&self) {
loop {
tokio::time::sleep(self.health_check_interval).await;
let mut pool = self.connections.lock().await;
pool.retain(|c| c.last_used.elapsed() < Duration::from_secs(300));
}
}
}
fn optimize_dispatch(
drivers: &[Driver],
requests: &[TripRequest],
constraints: &DispatchConstraints,
) -> Vec<Assignment> {
let mut assignments = Vec::new();
let mut available = drivers.to_vec();
let scored: Vec<_> = requests.iter()
.flat_map(|req| {
available.iter().map(move |driver| {
let eta = calculate_eta(driver, req);
let score = score_match(driver, req, eta);
(driver.id, req.id, score, eta)
})
})
.collect();
scored.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap());
for (driver_id, req_id, score, eta) in scored {
if !available.iter().any(|d| d.id == driver_id) {
continue;
}
if eta > constraints.max_eta {
continue;
}
assignments.push(Assignment {
driver_id, req_id, score, eta,
});
available.retain(|d| d.id != driver_id);
}
assignments
}
fn optimize_dispatch(
drivers: &[Driver],
requests: &[TripRequest],
constraints: &DispatchConstraints,
) -> Vec<Assignment> {
let mut assignments = Vec::new();
let mut available = drivers.to_vec();
let scored: Vec<_> = requests.iter()
.flat_map(|req| {
available.iter().map(move |driver| {
let eta = calculate_eta(driver, req);
let score = score_match(driver, req, eta);
(driver.id, req.id, score, eta)
})
})
.collect();
scored.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap());
for (driver_id, req_id, score, eta) in scored {
if !available.iter().any(|d| d.id == driver_id) {
continue;
}
if eta > constraints.max_eta {
continue;
}
assignments.push(Assignment {
driver_id, req_id, score, eta,
});
available.retain(|d| d.id != driver_id);
}
assignments
}
async function runETL(config: ETLConfig) {
const source = createSource(config.source);
const sink = createSink(config.destination);
let processed = 0;
let errors = 0;
for await (const batch of source.stream(1000)) {
const transformed = batch
.map(record => {
try {
const clean = normalize(record);
const valid = config.schema.parse(clean);
return { ok: true, data: valid };
} catch (e) {
errors++;
deadLetter.send(record, e);
return { ok: false };
}
})
.filter(r => r.ok)
.map(r => r.data);
await sink.writeBatch(transformed);
processed += transformed.length;
metrics.gauge("etl.processed", processed);
metrics.gauge("etl.errors", errors);
}
return { processed, errors };
}
async function runETL(config: ETLConfig) {
const source = createSource(config.source);
const sink = createSink(config.destination);
let processed = 0;
let errors = 0;
for await (const batch of source.stream(1000)) {
const transformed = batch
.map(record => {
try {
const clean = normalize(record);
const valid = config.schema.parse(clean);
return { ok: true, data: valid };
} catch (e) {
errors++;
deadLetter.send(record, e);
return { ok: false };
}
})
.filter(r => r.ok)
.map(r => r.data);
await sink.writeBatch(transformed);
processed += transformed.length;
metrics.gauge("etl.processed", processed);
metrics.gauge("etl.errors", errors);
}
return { processed, errors };
}
async function runAgentWorkflow(task: Task) {
const planner = new PlannerAgent({
model: "claude-sonnet-4-20250514",
tools: [searchTool, sqlTool, emailTool],
});
const plan = await planner.decompose(task);
const results: StepResult[] = [];
for (const step of plan.steps) {
const agent = AgentPool.acquire(step.type);
const result = await agent.execute(step, {
context: results,
timeout: 30_000,
retries: 2,
});
results.push(result);
if (result.requiresApproval) {
await HumanInTheLoop.request({
step,
result,
assignee: task.owner,
});
}
}
return planner.synthesize(results);
}
async function runAgentWorkflow(task: Task) {
const planner = new PlannerAgent({
model: "claude-sonnet-4-20250514",
tools: [searchTool, sqlTool, emailTool],
});
const plan = await planner.decompose(task);
const results: StepResult[] = [];
for (const step of plan.steps) {
const agent = AgentPool.acquire(step.type);
const result = await agent.execute(step, {
context: results,
timeout: 30_000,
retries: 2,
});
results.push(result);
if (result.requiresApproval) {
await HumanInTheLoop.request({
step,
result,
assignee: task.owner,
});
}
}
return planner.synthesize(results);
}
class IntakeProcessor:
def __init__(self, ocr, classifier, crm):
self.ocr = ocr
self.classifier = classifier
self.crm = crm
async def process(self, document):
text = await self.ocr.extract(document.file)
doc_type = await self.classifier.classify(text)
if doc_type == "insurance_card":
data = await self.extract_insurance(text)
await self.crm.update_insurance(
patient_id=document.patient_id,
carrier=data["carrier"],
policy_number=data["policy"],
group_number=data["group"],
)
elif doc_type == "referral":
data = await self.extract_referral(text)
await self.crm.create_referral(
patient_id=document.patient_id,
provider=data["referring_provider"],
reason=data["reason"],
valid_until=data["expiration"],
)
await self.audit_log(document, doc_type)
return doc_type
class IntakeProcessor:
def __init__(self, ocr, classifier, crm):
self.ocr = ocr
self.classifier = classifier
self.crm = crm
async def process(self, document):
text = await self.ocr.extract(document.file)
doc_type = await self.classifier.classify(text)
if doc_type == "insurance_card":
data = await self.extract_insurance(text)
await self.crm.update_insurance(
patient_id=document.patient_id,
carrier=data["carrier"],
policy_number=data["policy"],
group_number=data["group"],
)
elif doc_type == "referral":
data = await self.extract_referral(text)
await self.crm.create_referral(
patient_id=document.patient_id,
provider=data["referring_provider"],
reason=data["reason"],
valid_until=data["expiration"],
)
await self.audit_log(document, doc_type)
return doc_type
export async function processContract(
document: Buffer,
schema: ContractSchema,
) {
const pages = await pdf.parse(document);
const chunks = chunkBySection(pages, {
maxTokens: 2000,
overlap: 200,
});
const extractions = await Promise.all(
chunks.map(chunk =>
anthropic.messages.create({
model: "claude-sonnet-4-20250514",
messages: [{
role: "user",
content: [
{ type: "text", text: EXTRACTION_PROMPT },
{ type: "text", text: chunk.text },
],
}],
max_tokens: 4096,
})
)
);
const merged = mergeExtractions(extractions);
const validated = schema.parse(merged);
await db.contracts.upsert(validated);
return validated;
}
export async function processContract(
document: Buffer,
schema: ContractSchema,
) {
const pages = await pdf.parse(document);
const chunks = chunkBySection(pages, {
maxTokens: 2000,
overlap: 200,
});
const extractions = await Promise.all(
chunks.map(chunk =>
anthropic.messages.create({
model: "claude-sonnet-4-20250514",
messages: [{
role: "user",
content: [
{ type: "text", text: EXTRACTION_PROMPT },
{ type: "text", text: chunk.text },
],
}],
max_tokens: 4096,
})
)
);
const merged = mergeExtractions(extractions);
const validated = schema.parse(merged);
await db.contracts.upsert(validated);
return validated;
}
resource "aws_ecs_service" "api" {
name = "novostra-api-prod"
cluster = aws_ecs_cluster.main.id
task_definition = aws_ecs_task_definition.api.arn
desired_count = 3
launch_type = "FARGATE"
network_configuration {
subnets = var.private_subnets
security_groups = [aws_security_group.api.id]
assign_public_ip = false
}
load_balancer {
target_group_arn = aws_lb_target_group.api.arn
container_name = "api"
container_port = 8080
}
service_registries {
registry_arn = aws_service_discovery_service.api.arn
}
deployment_circuit_breaker {
enable = true
rollback = true
}
}
resource "aws_ecs_service" "api" {
name = "novostra-api-prod"
cluster = aws_ecs_cluster.main.id
task_definition = aws_ecs_task_definition.api.arn
desired_count = 3
launch_type = "FARGATE"
network_configuration {
subnets = var.private_subnets
security_groups = [aws_security_group.api.id]
assign_public_ip = false
}
load_balancer {
target_group_arn = aws_lb_target_group.api.arn
container_name = "api"
container_port = 8080
}
service_registries {
registry_arn = aws_service_discovery_service.api.arn
}
deployment_circuit_breaker {
enable = true
rollback = true
}
}
template<typename T, size_t Dim>
class VectorIndex {
HNSWGraph<Dim> graph_;
std::unordered_map<uint64_t, Metadata> meta_;
mutable std::shared_mutex mutex_;
public:
void insert(uint64_t id,
const Vector<Dim>& vec,
Metadata meta) {
std::unique_lock lock(mutex_);
graph_.add_point(vec.data(), id);
meta_[id] = std::move(meta);
}
std::vector<SearchResult> search(
const Vector<Dim>& query,
size_t top_k,
const Filter& filter = {}) const
{
std::shared_lock lock(mutex_);
auto neighbors = graph_.search(
query.data(), top_k * 2
);
std::vector<SearchResult> results;
for (auto& [id, dist] : neighbors) {
if (filter.matches(meta_.at(id))) {
results.push_back({id, dist, meta_.at(id)});
}
if (results.size() >= top_k) break;
}
return results;
}
};
template<typename T, size_t Dim>
class VectorIndex {
HNSWGraph<Dim> graph_;
std::unordered_map<uint64_t, Metadata> meta_;
mutable std::shared_mutex mutex_;
public:
void insert(uint64_t id,
const Vector<Dim>& vec,
Metadata meta) {
std::unique_lock lock(mutex_);
graph_.add_point(vec.data(), id);
meta_[id] = std::move(meta);
}
std::vector<SearchResult> search(
const Vector<Dim>& query,
size_t top_k,
const Filter& filter = {}) const
{
std::shared_lock lock(mutex_);
auto neighbors = graph_.search(
query.data(), top_k * 2
);
std::vector<SearchResult> results;
for (auto& [id, dist] : neighbors) {
if (filter.matches(meta_.at(id))) {
results.push_back({id, dist, meta_.at(id)});
}
if (results.size() >= top_k) break;
}
return results;
}
};
async def route_maintenance_request(request):
urgency = await classifier.assess_urgency(
description=request.description,
photos=request.attachments,
)
vendor = await find_best_vendor(
category=urgency.category,
location=request.unit.address,
budget=request.property.maintenance_budget,
)
work_order = await create_work_order(
request=request,
vendor=vendor,
priority=urgency.level,
estimated_cost=vendor.estimate,
)
await notify_tenant(
tenant=request.tenant,
work_order=work_order,
eta=vendor.next_available,
)
await notify_property_manager(
manager=request.property.manager,
work_order=work_order,
)
return work_order
async def route_maintenance_request(request):
urgency = await classifier.assess_urgency(
description=request.description,
photos=request.attachments,
)
vendor = await find_best_vendor(
category=urgency.category,
location=request.unit.address,
budget=request.property.maintenance_budget,
)
work_order = await create_work_order(
request=request,
vendor=vendor,
priority=urgency.level,
estimated_cost=vendor.estimate,
)
await notify_tenant(
tenant=request.tenant,
work_order=work_order,
eta=vendor.next_available,
)
await notify_property_manager(
manager=request.property.manager,
work_order=work_order,
)
return work_order
async def evaluate_models(
test_suite: TestSuite,
models: list[ModelConfig],
) -> EvalReport:
results = {}
for model in models:
client = init_client(model)
scores = []
for case in test_suite.cases:
response = await client.generate(
prompt=case.prompt,
temperature=0,
max_tokens=case.max_tokens,
)
score = await grade_response(
response=response.text,
expected=case.expected,
rubric=case.rubric,
grader_model="claude-sonnet-4-20250514",
)
scores.append(score)
results[model.name] = {
"accuracy": mean(s.accuracy for s in scores),
"latency_p50": percentile(s.latency for s in scores, 50),
"latency_p99": percentile(s.latency for s in scores, 99),
"cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000,
}
return EvalReport(results=results, suite=test_suite)
async def evaluate_models(
test_suite: TestSuite,
models: list[ModelConfig],
) -> EvalReport:
results = {}
for model in models:
client = init_client(model)
scores = []
for case in test_suite.cases:
response = await client.generate(
prompt=case.prompt,
temperature=0,
max_tokens=case.max_tokens,
)
score = await grade_response(
response=response.text,
expected=case.expected,
rubric=case.rubric,
grader_model="claude-sonnet-4-20250514",
)
scores.append(score)
results[model.name] = {
"accuracy": mean(s.accuracy for s in scores),
"latency_p50": percentile(s.latency for s in scores, 50),
"latency_p99": percentile(s.latency for s in scores, 99),
"cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000,
}
return EvalReport(results=results, suite=test_suite)
Your technical co-founder
We handle the engineering so you can focus on growth. Prototype to production.
Book a CallSound familiar?
No engineering team
You have the vision and the market. You need a team that can turn that into a production-ready product.
Product that doesn't scale
The current version works but it won't handle growth. You need it rebuilt properly, not from scratch.
Hiring takes too long
You need to ship now but building an in-house team takes months of recruiting, interviewing, and onboarding.
Legacy system holding you back
The old system still runs the business but it's slow, brittle, and no one wants to touch it. You need a path forward.
How we solve it
We map your product vision, requirements, data sources, constraints. You get an architecture document with a clear project plan.
What we build
Full-stack product development
We build the complete product. Frontend, backend, databases, and APIs. Architecture that scales from day one.
API and data integrations
Connect to third-party APIs and normalize messy data sources into a clean foundation.
Cloud deployment and CI/CD
AWS, GCP, or Azure. Automated deployments, monitoring, and infrastructure that grows with you.
Technical architecture
Clean, documented system design. Ready to scale and ready to hand off.
AI-powered features
LLM integrations, AI agents, and intelligent features built into your product from the start.
Product design and UX
User research, wireframes, and polished interfaces. We design for real users, not just stakeholder demos.
Tech stack we use
OpenClaw
OpenAI
Anthropic
Google Gemini
Meta
Hugging Face
LangChain
PyTorch
TensorFlow
Scikit-learn
Keras
OpenCV
Pinecone
Weaviate
ElevenLabs
Runway
Stability AI
Replicate
Weights & Biases
MLflow
Pandas
NumPy
Jupyter
Kaggle- BUILT FOR YOU, NOT A TEMPLATE
- MEASURABLE RESULTS
- DOMAIN EXPERTISE FIRST
- PRODUCTION-GRADE
- STAY LEAN, SHIP FAST
- BIWEEKLY DEMOS, FULL VISIBILITY
- CLEAN HANDOFF, NO LOCK-IN
- WE BECOME YOUR TEAM
- BUILT FOR YOU, NOT A TEMPLATE
- MEASURABLE RESULTS
- DOMAIN EXPERTISE FIRST
- PRODUCTION-GRADE
- STAY LEAN, SHIP FAST
- BIWEEKLY DEMOS, FULL VISIBILITY
- CLEAN HANDOFF, NO LOCK-IN
- WE BECOME YOUR TEAM
Case studies
Evaluation Platform for a Conversational AI Startup
Happyverse · AI / SaaS
30+
Providers Integrated
Week → Same day
Evaluation Cycle
Dispatch Automation for a 115-Vehicle Fleet
Best Ride · Medical Transportation
<6h+ → 2h
Dispatcher Phone Time
<2 min
Callout Recovery
AI Front Office for a Psychiatry Practice
MD Psychiatry Clinic · Healthcare
22% → 13%
No-Show Rate
<3%
Missed Calls
AI Tooling Strategy for a Software Consulting Firm
Rafael Technology, LLC · Technology
2x Faster
Feature Delivery
40% → 75%
Test Coverage
Evaluation Platform for a Conversational AI Startup
Happyverse · AI / SaaS
30+
Providers Integrated
Week → Same day
Evaluation Cycle
Dispatch Automation for a 115-Vehicle Fleet
Best Ride · Medical Transportation
<6h+ → 2h
Dispatcher Phone Time
<2 min
Callout Recovery
AI Front Office for a Psychiatry Practice
MD Psychiatry Clinic · Healthcare
22% → 13%
No-Show Rate
<3%
Missed Calls
AI Tooling Strategy for a Software Consulting Firm
Rafael Technology, LLC · Technology
2x Faster
Feature Delivery
40% → 75%
Test Coverage
Evaluation Platform for a Conversational AI Startup
Happyverse · AI / SaaS
30+
Providers Integrated
Week → Same day
Evaluation Cycle
Dispatch Automation for a 115-Vehicle Fleet
Best Ride · Medical Transportation
<6h+ → 2h
Dispatcher Phone Time
<2 min
Callout Recovery
AI Front Office for a Psychiatry Practice
MD Psychiatry Clinic · Healthcare
22% → 13%
No-Show Rate
<3%
Missed Calls
AI Tooling Strategy for a Software Consulting Firm
Rafael Technology, LLC · Technology
2x Faster
Feature Delivery
40% → 75%
Test Coverage