// 03 · Service
LLM integration & evaluation
Models wired into your product behind an interface you control, provider-agnostic by design. Every prompt and model change is gated on evals — measurable quality, not vibes.
- OpenAI
- Anthropic
- Gemini
- LangChain
// The problem
Why this is hard
Wiring an LLM into your product is easy; keeping it good is hard. A prompt tweak that helps one case quietly breaks three others, a provider outage takes your feature down, and 'is this better?' becomes a matter of opinion. Without an eval suite and a provider-agnostic interface, every change is a gamble and every vendor is a single point of failure.
// What we build
What you get
Provider-agnostic abstraction
An interface you control, so you can switch or blend providers without rewriting your product.
Eval & regression suite
Every prompt and model change gated on a measurable quality bar — no more 'seems better'.
Prompt & cost management
Versioned prompts and a cost/latency budget wired in, so quality and spend are both visible.
Streaming & structured output
Reliable structured output and streaming UX, validated against your schema.
// How it fits together
The system we build
- Appyour product
- Provider abstractionswap / blend
- Prompt + schemaversioned
- Model callstreaming, structured
- Eval gateregression-checked
// Deliverables
- Provider-agnostic abstraction
- Eval & regression suite
- Prompt and cost management
- Streaming & structured output
// How we work
From prototype to production, in four moves.
Discovery
We map the problem, the data, and the eval that defines "done".
Prototype
A working slice in weeks — real model, real data, measured.
Production
Hardened, observable, evaluable. Shipped where users live.
Scale
Cost, latency and reliability tuned as load and scope grow.
// Typical engagement
What it takes to work together
Architecture Sprint
$4–8kfixed · 1–2 wks
1–2 weeks
De-risk before you build — architecture, a plan, and a working proof-of-concept.
Build
from$20kfixed scope or pod
typically 1–3 months
Ship the product end-to-end, in your repo and conventions.
Run & Scale
from$4k/ month
ongoing
Operate and improve after launch — SLA, monitoring, and iteration.
Indicative ranges — the final price and timeline depend on your project's scope, complexity, and integrations. A paid Architecture Sprint pins them down.
// Proof
Shipped in production
Fintech — Document intelligence
manual review
vs. the fully-manual baseline
throughput per analyst
vs. the pre-automation baseline
// FAQ
Common questions
// Related
RAG & Knowledge Systems
Retrieval grounded in your own data, with an eval harness that proves the answers are faithful to the source — not plausible-sounding guesses.
MLOps & Infra
The queues, workers, observability, and cost controls that keep models fast, reliable, and on budget once real traffic arrives.
AI Agents & Automation
Tool-using agents that act on your systems — calling your APIs, running workflows, deciding what to do next — with guardrails, retries, and traces so they are safe to run unattended in production.
Product Engineering
Full-stack, AI-native products from first prototype to a system users trust — designed, built, and shipped in your repo and your conventions.
AI Strategy & Audit
A clear-eyed read on where AI pays off and where it does not — an architecture review, an honest risk and feasibility assessment, and a prioritised roadmap before you commit budget.
// Industries
Where teams put this to work:
// Let's build
Ready to build with llm integration & evals?
Tell us where you are. We reply within a day with a concrete next step.