Model Gateway Deployment
Deploy a unified inference gateway with provider abstraction, request shaping, and per-tenant rate limits. Traffic flows through OpenTelemetry-instrumented proxies with structured error codes mapped to retry policies.
services_directory
Production AI capabilities, organized by execution depth.
Services are tiered from integration through custom pipelines to system optimization. Teams typically enter at Core AI Integration, expand into pipeline engineering as data volume grows, and engage optimization once production SLOs and cost envelopes are under load.
Schedule Architecture Calltechnical_tiers
Service directory by tier
[tier_01]
Core AI Integration
Embed model capabilities behind stable APIs with auth, routing, and observability from day one.
RAG Retrieval Layer
Stand up hybrid retrieval with dense embeddings, BM25 fallback, and cross-encoder reranking. Document ingestion runs through chunked parsers with lineage metadata stored in Postgres; query paths enforce ACL filters before context assembly.
Tool-Calling API Surface
Expose typed tool contracts via OpenAPI schemas with JSON-schema validation on inputs and outputs. Tool invocations execute in isolated workers with timeout budgets, idempotency keys, and audit logs written to an immutable event store.
[tier_02]
Custom Pipelines
Build data and evaluation pipelines that keep models honest as distributions shift in production.
Event-Driven Ingestion
Stream documents and telemetry through Kafka topics into bronze-silver-gold lakehouse tables. Schema registry enforces Avro contracts; deduplication keys prevent double-processing during consumer group rebalances.
Eval and Labeling Loops
Automate regression suites against golden datasets with human review queues for edge cases. Failed evals block promotion in CI; labelers work from diff views comparing model outputs across prompt versions.
Feature Store Sync
Materialize online features from batch pipelines with point-in-time correctness for training sets. Serving paths read from Redis with sub-10ms p99; offline stores backfill via Spark jobs scheduled on Airflow DAGs.
[tier_03]
System Optimization
Tune cost, latency, and reliability once systems carry real traffic and SLO commitments.
Latency and Cost Profiling
Instrument end-to-end traces from API gateway through retrieval, inference, and post-processing. Heatmaps isolate token-heavy prompts; recommendations include prompt compression, model routing tiers, and batch window adjustments.
Autoscaling Policy Design
Configure HPA and KEDA scalers on queue depth, GPU utilization, and custom metrics exported from Prometheus. Scale-down cooldowns prevent thrashing; pre-warm hooks load model weights before traffic spikes.
Observability Hardening
Wire SLO dashboards, burn-rate alerts, and synthetic probes against critical inference paths. Log pipelines redact PII at ingest; incident runbooks map alert fingerprints to rollback and failover procedures.
engagement_process
From architecture review to 0-to-1 production
Engagements follow a fixed sequence so discovery artifacts directly inform build specifications. Each phase ends with signed deliverables before the next phase begins.
- [01]
Discovery and Architecture Review
We map your current AI surface area: prompt chains, ad-hoc scripts, vendor dependencies, and undeclared SLOs. Sessions produce a current-state diagram, dependency graph, and prioritized risk register covering data leakage, cost runaway, and non-deterministic outputs.
deliverables- — Current-state system diagram
- — SLO and error-budget inventory
- — Risk register with severity rankings
- — Integration boundary recommendations
- [02]
Technical Blueprint
Architecture review findings convert into buildable specifications: sequence diagrams for request paths, data contracts between services, and an infrastructure bill of materials with environment topology (VPC, IAM, secrets, networking).
deliverables- — Sequence diagrams for primary flows
- — OpenAPI and event schema definitions
- — Infrastructure BOM and environment map
- — Eval strategy and acceptance criteria
- [03]
Foundation Sprint
Before feature velocity, we establish the production substrate: CI/CD pipelines, staging parity with production networking, centralized logging, and deploy gates tied to eval pass rates.
deliverables- — CI/CD pipeline with preview environments
- — OpenTelemetry tracing and metrics baseline
- — Secrets management and IAM least-privilege roles
- — Staging environment mirroring production topology
- [04]
0-to-1 Production Build
A vertical slice ships to production with typed APIs, retrieval or agent orchestration, and human-in-the-loop checkpoints where required. Each merge requires eval suite passage; rollbacks are one-command with versioned prompt and model artifacts.
deliverables- — Production-deployed vertical slice
- — Automated eval gates in deploy pipeline
- — Runbook for incident triage
- — Load-test report against target SLOs
- [05]
Handoff and Optimization
We transfer operational ownership with documentation your team can maintain. A follow-on optimization pass targets measured latency and token spend using production traces—not benchmark anecdotes.
deliverables- — On-call playbooks and escalation paths
- — Cost and latency optimization report
- — Knowledge transfer sessions with engineering leads
- — 30-day post-launch review and backlog recommendations
Ready for an architecture review?
Start with a structured assessment of your current AI stack. We will return a blueprint and phased build plan within two weeks of kickoff.