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 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 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);
}
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;
}
};
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;
async def evaluate_models(
test_suite: TestSuite,
models: list[ModelConfig],
) -> EvalReport:
results = {}
for model in models:
client = init_client(model)
scores = []
for case in test_suite.cases:
response = await client.generate(
prompt=case.prompt,
temperature=0,
max_tokens=case.max_tokens,
)
score = await grade_response(
response=response.text,
expected=case.expected,
rubric=case.rubric,
grader_model="claude-sonnet-4-20250514",
)
scores.append(score)
results[model.name] = {
"accuracy": mean(s.accuracy for s in scores),
"latency_p50": percentile(s.latency for s in scores, 50),
"latency_p99": percentile(s.latency for s in scores, 99),
"cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000,
}
return EvalReport(results=results, suite=test_suite)
async def evaluate_models(
test_suite: TestSuite,
models: list[ModelConfig],
) -> EvalReport:
results = {}
for model in models:
client = init_client(model)
scores = []
for case in test_suite.cases:
response = await client.generate(
prompt=case.prompt,
temperature=0,
max_tokens=case.max_tokens,
)
score = await grade_response(
response=response.text,
expected=case.expected,
rubric=case.rubric,
grader_model="claude-sonnet-4-20250514",
)
scores.append(score)
results[model.name] = {
"accuracy": mean(s.accuracy for s in scores),
"latency_p50": percentile(s.latency for s in scores, 50),
"latency_p99": percentile(s.latency for s in scores, 99),
"cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000,
}
return EvalReport(results=results, suite=test_suite)
class 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
]
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
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)
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
}
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;
}
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));
}
}
}
export async function POST(req: Request) {
const { messages, tools } = await req.json();
const session = await auth.verify(req);
const stream = await anthropic.messages.stream({
model: "claude-sonnet-4-20250514",
max_tokens: 4096,
system: buildSystemPrompt(session),
messages,
tools: tools ?? defaultTools,
});
const encoder = new TextEncoder();
const readable = new ReadableStream({
async start(controller) {
for await (const event of stream) {
const data = JSON.stringify(event);
controller.enqueue(
encoder.encode("data: " + data + "\n\n")
);
}
controller.close();
},
});
return new Response(readable, {
headers: {
"Content-Type": "text/event-stream",
"Cache-Control": "no-cache",
},
});
}
export async function POST(req: Request) {
const { messages, tools } = await req.json();
const session = await auth.verify(req);
const stream = await anthropic.messages.stream({
model: "claude-sonnet-4-20250514",
max_tokens: 4096,
system: buildSystemPrompt(session),
messages,
tools: tools ?? defaultTools,
});
const encoder = new TextEncoder();
const readable = new ReadableStream({
async start(controller) {
for await (const event of stream) {
const data = JSON.stringify(event);
controller.enqueue(
encoder.encode("data: " + data + "\n\n")
);
}
controller.close();
},
});
return new Response(readable, {
headers: {
"Content-Type": "text/event-stream",
"Cache-Control": "no-cache",
},
});
}
func (h *DispatchHub) Run() {
for {
select {
case driver := <-h.register:
h.drivers[driver.ID] = driver
h.broadcastDriverCount()
case driver := <-h.unregister:
delete(h.drivers, driver.ID)
close(driver.Send)
h.broadcastDriverCount()
case update := <-h.locationUpdates:
h.drivers[update.DriverID].Location = update.Position
h.grid.Update(update.DriverID, update.Position)
nearby := h.grid.FindNearby(
update.Position, 5*time.Minute,
)
h.pendingRequests.TryMatch(nearby)
case req := <-h.tripRequests:
candidates := h.grid.FindNearby(
req.Pickup, 10*time.Minute,
)
if best := h.scorer.Rank(candidates, req); best != nil {
h.assign(best, req)
} else {
h.pendingRequests.Enqueue(req)
}
}
}
}
func (h *DispatchHub) Run() {
for {
select {
case driver := <-h.register:
h.drivers[driver.ID] = driver
h.broadcastDriverCount()
case driver := <-h.unregister:
delete(h.drivers, driver.ID)
close(driver.Send)
h.broadcastDriverCount()
case update := <-h.locationUpdates:
h.drivers[update.DriverID].Location = update.Position
h.grid.Update(update.DriverID, update.Position)
nearby := h.grid.FindNearby(
update.Position, 5*time.Minute,
)
h.pendingRequests.TryMatch(nearby)
case req := <-h.tripRequests:
candidates := h.grid.FindNearby(
req.Pickup, 10*time.Minute,
)
if best := h.scorer.Rank(candidates, req); best != nil {
h.assign(best, req)
} else {
h.pendingRequests.Enqueue(req)
}
}
}
}
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 };
}
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
}
}
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 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 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);
}
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;
}
};
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;
async def evaluate_models(
test_suite: TestSuite,
models: list[ModelConfig],
) -> EvalReport:
results = {}
for model in models:
client = init_client(model)
scores = []
for case in test_suite.cases:
response = await client.generate(
prompt=case.prompt,
temperature=0,
max_tokens=case.max_tokens,
)
score = await grade_response(
response=response.text,
expected=case.expected,
rubric=case.rubric,
grader_model="claude-sonnet-4-20250514",
)
scores.append(score)
results[model.name] = {
"accuracy": mean(s.accuracy for s in scores),
"latency_p50": percentile(s.latency for s in scores, 50),
"latency_p99": percentile(s.latency for s in scores, 99),
"cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000,
}
return EvalReport(results=results, suite=test_suite)
async def evaluate_models(
test_suite: TestSuite,
models: list[ModelConfig],
) -> EvalReport:
results = {}
for model in models:
client = init_client(model)
scores = []
for case in test_suite.cases:
response = await client.generate(
prompt=case.prompt,
temperature=0,
max_tokens=case.max_tokens,
)
score = await grade_response(
response=response.text,
expected=case.expected,
rubric=case.rubric,
grader_model="claude-sonnet-4-20250514",
)
scores.append(score)
results[model.name] = {
"accuracy": mean(s.accuracy for s in scores),
"latency_p50": percentile(s.latency for s in scores, 50),
"latency_p99": percentile(s.latency for s in scores, 99),
"cost_per_1k": sum(s.cost for s in scores) / len(scores) * 1000,
}
return EvalReport(results=results, suite=test_suite)
class 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
]
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
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)
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
}
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;
}
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));
}
}
}
export async function POST(req: Request) {
const { messages, tools } = await req.json();
const session = await auth.verify(req);
const stream = await anthropic.messages.stream({
model: "claude-sonnet-4-20250514",
max_tokens: 4096,
system: buildSystemPrompt(session),
messages,
tools: tools ?? defaultTools,
});
const encoder = new TextEncoder();
const readable = new ReadableStream({
async start(controller) {
for await (const event of stream) {
const data = JSON.stringify(event);
controller.enqueue(
encoder.encode("data: " + data + "\n\n")
);
}
controller.close();
},
});
return new Response(readable, {
headers: {
"Content-Type": "text/event-stream",
"Cache-Control": "no-cache",
},
});
}
export async function POST(req: Request) {
const { messages, tools } = await req.json();
const session = await auth.verify(req);
const stream = await anthropic.messages.stream({
model: "claude-sonnet-4-20250514",
max_tokens: 4096,
system: buildSystemPrompt(session),
messages,
tools: tools ?? defaultTools,
});
const encoder = new TextEncoder();
const readable = new ReadableStream({
async start(controller) {
for await (const event of stream) {
const data = JSON.stringify(event);
controller.enqueue(
encoder.encode("data: " + data + "\n\n")
);
}
controller.close();
},
});
return new Response(readable, {
headers: {
"Content-Type": "text/event-stream",
"Cache-Control": "no-cache",
},
});
}
func (h *DispatchHub) Run() {
for {
select {
case driver := <-h.register:
h.drivers[driver.ID] = driver
h.broadcastDriverCount()
case driver := <-h.unregister:
delete(h.drivers, driver.ID)
close(driver.Send)
h.broadcastDriverCount()
case update := <-h.locationUpdates:
h.drivers[update.DriverID].Location = update.Position
h.grid.Update(update.DriverID, update.Position)
nearby := h.grid.FindNearby(
update.Position, 5*time.Minute,
)
h.pendingRequests.TryMatch(nearby)
case req := <-h.tripRequests:
candidates := h.grid.FindNearby(
req.Pickup, 10*time.Minute,
)
if best := h.scorer.Rank(candidates, req); best != nil {
h.assign(best, req)
} else {
h.pendingRequests.Enqueue(req)
}
}
}
}
func (h *DispatchHub) Run() {
for {
select {
case driver := <-h.register:
h.drivers[driver.ID] = driver
h.broadcastDriverCount()
case driver := <-h.unregister:
delete(h.drivers, driver.ID)
close(driver.Send)
h.broadcastDriverCount()
case update := <-h.locationUpdates:
h.drivers[update.DriverID].Location = update.Position
h.grid.Update(update.DriverID, update.Position)
nearby := h.grid.FindNearby(
update.Position, 5*time.Minute,
)
h.pendingRequests.TryMatch(nearby)
case req := <-h.tripRequests:
candidates := h.grid.FindNearby(
req.Pickup, 10*time.Minute,
)
if best := h.scorer.Rank(candidates, req); best != nil {
h.assign(best, req)
} else {
h.pendingRequests.Enqueue(req)
}
}
}
}
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 };
}
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
}
}
Make your dev team
ship 2x faster
We audit your workflow, recommend the right AI dev tools, train your team on their actual codebase, and keep you current as the landscape evolves.
Get a Free AssessmentSound familiar?
Every developer uses different tools
No standardization across the team. Different tools, different prompts, different workflows. No shared playbook.
No time to evaluate new tools
New AI dev tools launch weekly. Your team is at capacity shipping features. The burden of evaluation falls on people who can't afford the distraction.
Low adoption, unclear ROI
You bought licenses but most developers use 20% of what's available. Leadership can't measure if the investment is paying off.
Generic training doesn't stick
Vendor workshops teach general features. Your team needs to know how to use these tools on their actual codebase, their actual workflows.
Tools we evaluate
Claude Code
Cursor
Codex
GitHub Copilot
Windsurf
Devin
Gemini Code Assist
JetBrains
Aider
v0
Lovable
Bolt
Replit
Base44
CodeRabbit
Qodo
n8nHow we solve it
We map your team's current workflow, tool usage, and bottlenecks. What's manual that shouldn't be? Where are developers losing time?
What you get
Measurable productivity gains
We measure results: faster delivery, fewer bugs, reduced cycle time.
Internal playbooks
Custom documentation and prompt libraries built on your actual codebase. Specific, actionable, immediately useful.
Competitive edge
Your team stays ahead of competitors still figuring out which tools to use.
Case studies
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
Evaluation Platform for a Conversational AI Startup
Happyverse · AI / SaaS
30+
Providers Integrated
Week → Same day
Evaluation Cycle
AI Front Office for a Psychiatry Practice
MD Psychiatry Clinic · Healthcare
22% → 13%
No-Show Rate
<3%
Missed Calls
AI Tooling Strategy for a Software Consulting Firm
Rafael Technology, LLC · Technology
2x Faster
Feature Delivery
40% → 75%
Test Coverage
Dispatch Automation for a 115-Vehicle Fleet
Best Ride · Medical Transportation
<6h+ → 2h
Dispatcher Phone Time
<2 min
Callout Recovery
Evaluation Platform for a Conversational AI Startup
Happyverse · AI / SaaS
30+
Providers Integrated
Week → Same day
Evaluation Cycle
AI Front Office for a Psychiatry Practice
MD Psychiatry Clinic · Healthcare
22% → 13%
No-Show Rate
<3%
Missed Calls
AI Tooling Strategy for a Software Consulting Firm
Rafael Technology, LLC · Technology
2x Faster
Feature Delivery
40% → 75%
Test Coverage
Dispatch Automation for a 115-Vehicle Fleet
Best Ride · Medical Transportation
<6h+ → 2h
Dispatcher Phone Time
<2 min
Callout Recovery
Evaluation Platform for a Conversational AI Startup
Happyverse · AI / SaaS
30+
Providers Integrated
Week → Same day
Evaluation Cycle
AI Front Office for a Psychiatry Practice
MD Psychiatry Clinic · Healthcare
22% → 13%
No-Show Rate
<3%
Missed Calls