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
]
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));
}
}
}
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"],
},
}
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)
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 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 };
}
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
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)
}
}
}
}
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
}
}
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;
}
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 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);
}
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",
},
});
}
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;
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
}
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)
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
]
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));
}
}
}
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"],
},
}
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)
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 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 };
}
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
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)
}
}
}
}
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
}
}
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;
}
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 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);
}
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",
},
});
}
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;
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
}
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)
Your team spends hours on tasks that should take minutes
We find the bottlenecks, design the automation, and build it. From the first audit to production deployment, we own the entire process.
Get a Free Workflow AuditSound familiar?
Staff on the phone all day
Your best people spend hours answering the same questions, confirming appointments, and relaying updates. The work that grows your business waits.
Data scattered, no source of truth
Client info lives in spreadsheets, email threads, your CRM, and someone's notebook. Every handoff is a chance for something to fall through the cracks.
Processes that break when someone is out
When the person who 'just knows how' is out, the workflow stops. Nothing is documented because the process is the person.
No-shows and scheduling conflicts
Appointments get double-booked, reminders go out late or not at all, and empty slots mean lost revenue.
How we solve it
We map your current operations end-to-end. Every manual step, every handoff, every workaround. We identify what to automate first based on time saved and business impact.
Who we help
Any business with 5-200 employees running on a mix of phone calls, emails, spreadsheets, and manual processes. Same pain, custom solution.
Medical practices
Scheduling, intake, follow-ups, insurance verification, and referral management.
Reduce front desk burden and no-shows.
Law firms
Client intake, document assembly, billing, court filing prep, and follow-up sequences.
Reclaim billable hours.
Property management
Tenant comms, maintenance requests, lease renewals, vendor coordination, and reporting.
Manage more units with the same team.
Logistics companies
Dispatching, driver communications, load tracking, invoicing, and customer status updates.
Less manual coordination.
Accounting firms
Document collection, data entry, reconciliation, client onboarding, and deadline tracking.
Less admin, more advisory.
Insurance agencies
Policy renewals, claims intake, compliance docs, follow-ups, and carrier communications.
Faster processing, fewer errors.
Case studies
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
Evaluation Platform for a Conversational AI Startup
Happyverse · AI / SaaS
30+
Providers Integrated
Week → Same day
Evaluation Cycle
AI Tooling Strategy for a Software Consulting Firm
Rafael Technology, LLC · Technology
2x Faster
Feature Delivery
40% → 75%
Test Coverage
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
Evaluation Platform for a Conversational AI Startup
Happyverse · AI / SaaS
30+
Providers Integrated
Week → Same day
Evaluation Cycle
AI Tooling Strategy for a Software Consulting Firm
Rafael Technology, LLC · Technology
2x Faster
Feature Delivery
40% → 75%
Test Coverage
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
Evaluation Platform for a Conversational AI Startup
Happyverse · AI / SaaS
30+
Providers Integrated
Week → Same day
Evaluation Cycle
AI Tooling Strategy for a Software Consulting Firm
Rafael Technology, LLC · Technology
2x Faster
Feature Delivery
40% → 75%
Test Coverage