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Production AI systems and digital engineering for enterprise teams.Explore our work
Production AI systems and digital engineering for enterprise teams.Explore our work
Integrate OpenAI, Anthropic, and custom models into your platform with secure APIs, observability, and cost-aware architecture.
Teams we build with
Integrating models ad hoc creates security holes, unpredictable bills, and fragile prompts scattered across the codebase.
How we approach it
A unified LLM layer with routing, observability, and compliance hooks your platform team can govern.
How we deliver
We centralize model access, standardize telemetry, and document upgrade paths before scaling traffic.
Scope varies by engagement—these are the capabilities we most often deliver on projects like yours.
Multi-model routing and fallbacks
RAG and retrieval pipelines
Latency and cost optimization
Enterprise compliance readiness
Typical technology stack
Map scope, milestones, and team shape in one call.
Contact usSystem design
A typical stack for this practice—adapted to your compliance, cloud, and team constraints.
Stack layers
Narrower layers are closer to the user · wider layers are platform depth
Layer 1 · Gateway
Single front door for all model calls—authentication, quotas, logging, and schema validation before traffic hits providers.
Components & tools(9)
Repeatable practices that keep quality high across milestones—not one-off heroics.
Right model for each task with fallbacks.
Keys rotated and scoped per environment.
Budgets and alerts before finance surprises.
Data flow diagrams for security reviews.
Quality gates
Non-negotiable quality gates we apply before every release—not a post-launch checklist.
6 checkpoints on typical engagements
Standard 1: Single SDK/gateway for all model calls
Standard 2: Redact PII before inference
Standard 3: Version prompts in code review
Standard 4: Load-test retrieval at peak document size
Standard 5: Document data retention per vendor
Standard 6: Require security sign-off on new data flows before production traffic
Common questions about llm integration engagements.
When you need production AI—not a demo—with clear guardrails, observability, and a path to integrate with your existing product and data stack.
Timelines depend on scope and integrations. We define phased milestones in week one—typically a discovery sprint, build cycles with demos, then hardening and launch support.
We embed with your product and engineering leads through shared roadmaps, async updates, and structured reviews. You keep ownership of the codebase and infrastructure.
A 30-minute discovery call, then a short technical assessment and proposal with scope, team shape, and risks—no lengthy RFP process unless you need one.
30-minute call. We'll tell you if we're the right team—and what we'd do in the first two weeks.