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)
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));
}
}
}
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)
}
}
}
}
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 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"],
},
}
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
}
}
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
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 };
}
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 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;
}
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
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",
},
});
}
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)
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 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)
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));
}
}
}
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)
}
}
}
}
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 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"],
},
}
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
}
}
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
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 };
}
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 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;
}
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
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",
},
});
}
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)
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;
Production-grade AI systems
From multi-agent orchestration to data pipelines and governance. We build AI infrastructure that runs in production.
Book a CallSound familiar?
AI pilots that never reach production
The proof of concept worked in a notebook. Six months later it's still not deployed.
Data too messy to build on
Your data lives in silos, formats don't match, quality is inconsistent. Reliable AI needs reliable data.
No governance framework
Regulators are asking questions. Your board wants answers. You need audit trails, explainability, and compliance documentation before you scale.
Vendor lock-in with black-box models
You're building on APIs you don't control. Pricing changes, model deprecations, and outages are risks you can't mitigate.
How we solve it
We evaluate your data readiness, existing infrastructure, and business objectives. You get a clear picture of what's possible and what to do first.
What we deliver
AI strategy and roadmap
We identify where AI creates real value in your operations and build a roadmap your team can execute.
Data infrastructure and pipelines
Real-time and batch pipelines that feed your AI systems. Clean, reliable, built for the throughput your business demands.
Multi-agent orchestration
Autonomous agents that execute multi-step workflows with human-in-the-loop controls. Research, outreach, analysis, and decision support at scale.
RAG and knowledge systems
Connect LLMs to your internal knowledge base. Accurate, grounded answers from your own data.
NLP and document processing
Extract, classify, and summarize from contracts, medical records, legal filings, and unstructured data at scale.
AI governance and compliance
Model documentation, bias auditing, explainability reports. HIPAA, SOC 2, FDA, and industry-specific frameworks.
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