Production LLM plumbing without betting the company on one provider.
Production integrations with the major LLM providers — routing, failover, observability, and cost controls included.
- Typical duration
- 6–10 weeks
- Squad
- 2–3 senior engineers
- Starting at
- $18,000 fixed scope
- Provider-outage downtime absorbed
- 100%Provider-outage downtime absorbed
- LLM spend reduction via routing
- −41%LLM spend reduction via routing
- P95 first-token latency
- 380msP95 first-token latency
Calling an API is easy. Depending on one in production is not.
The first integration takes an afternoon. Then comes rate limiting, streaming edge cases, provider outages at the worst possible hour, cost that nobody can attribute to a feature, and a model deprecation notice with sixty days' warning. The integration layer is where that operational reality gets absorbed.
Sound familiar?
- A provider outage that takes your product down with it
- LLM spend that can't be attributed to a team or feature
- Model deprecations that force emergency rewrites
- Streaming and tool-call handling reimplemented in five services
How we deliver llm integrations.
Four phases, each with a written definition of done. You will always know which phase we are in and what has to be true to leave it.
Abstract
One internal interface across providers — so swapping a model is a config change rather than a refactor of every calling service.
Route
Per-task model routing on cost, latency, and capability, with automatic failover when a provider degrades.
Observe
Every call traced with prompt version, token counts, latency, and cost attributed down to the feature and the team.
Govern
Budgets, rate limits, PII redaction, and prompt-version control — enforced centrally so no service can bypass them.
What is included.
Every engagement is scoped to your problem, but these are the capabilities we bring to the table.
Provider routing
Task-aware routing across Claude, GPT, Gemini, and open-weight models — cheap models for cheap steps, frontier models where it counts.
Failover & resilience
Automatic cross-provider failover, circuit breakers, and backpressure so an upstream incident degrades rather than destroys.
Streaming & tool calls
One correct implementation of streaming, partial tool-call assembly, and cancellation — reused everywhere instead of rewritten.
Cost telemetry
Token and dollar attribution per feature, per team, and per customer, with alerts before the month-end invoice surprises anyone.
Prompt versioning
Prompts as versioned, reviewable artifacts with A/B rollout and one-click rollback when a change regresses quality.
Safety & redaction
PII detection and redaction before egress, plus configurable content policies enforced on both the request and the response.
Technology we typically reach for.
Chosen per engagement against your constraints — never because it is the fashionable choice this quarter.
- Claude
- OpenAI
- Gemini
- TypeScript
- Node.js
- Redis
- OpenTelemetry
What this looks like in production.
Grounding an AI research analyst on a decade of data
Atlas Capital
- Problem
- Analysts spent 60% of their day searching filings, transcripts, and broker notes for context.
- Solution
- A citation-grounded RAG system with role-aware controls, evaluated against a 1,200-question benchmark.
- Outcome
- Analyst throughput doubled on coverage tasks and onboarding time for new hires dropped by half.
- Analyst throughput
- 2xAnalyst throughput
- Onboarding time
- −50%Onboarding time
- Answer grounding
- 97%Answer grounding
Capabilities that pair well with this one.
Tell us what you're trying to solve.
A 30-minute call with a senior engineer — no SDRs, no discovery deck. You will leave with an honest read on whether this is the right capability and what it would take.