AI development that ships into real products, not demo videos.
Custom LLM features wired into the workflows you already have — measured, guarded, and portable across providers.
The AI features that actually ship share three traits: they're scoped to a specific job, they're measured before they launch, and they don't trap you in a single provider. We build to that bar. Summarization, structured extraction, semantic search, drafting, classification, and grounded Q&A — built into your existing product with evaluation harnesses, guardrails, and a fallback path. We're builders, not consultants — there's no "AI strategy" deck at the end of this engagement.
- Teams pasting things into ChatGPT and copying answers back by hand
- Existing AI features that hallucinate too often to ship
- Paralysis between OpenAI, Anthropic, Google, and the open-source models
- AI prototypes that never became features because cost or latency killed them
- Compliance or privacy concerns blocking AI use entirely
Product teams with a working product and a clearly repetitive task, companies that need AI features inside an existing workflow (not a chatbot), CTOs who need honest answers about cost, latency, and accuracy before committing, and teams that want AI capabilities without giving up control of their stack.
What is included
The shape of a typical engagement. We can adjust, narrow, or expand the scope before any work starts.
- Use-case scoping and feasibility analysis
- Evaluation harness built on your real data
- LLM orchestration (OpenAI, Anthropic, Google, open-source via vLLM / Ollama)
- Structured outputs and tool calling
- Retrieval-augmented generation (RAG) with the right vector store
- Guardrails — schema validation, refusal handling, fallbacks
- Cost and latency budgets instrumented from day one
- Provider-agnostic architecture — switchable without a rewrite
- Monitoring of accuracy, cost, and usage in production
How the engagement runs
One-week increments, running software every Friday, and a written scope you can keep.
Eval first
Before touching a model, we measure the current task: accuracy, time taken, error modes. This is our baseline.
Prototype on real data
Not a toy dataset. We test against your actual inputs.
Ship with guardrails
Structured outputs, validation, fallback paths, and cost caps.
Monitor in production
Accuracy, cost, and latency tracked continuously.
Tune and extend
Iterate based on real traffic, not vibes.
What you walk away with
Everything is handed back. Your code, your accounts, your documentation.
- A working AI feature integrated into your product
- An evaluation harness with baseline and current metrics
- Provider-portable code — no vendor lock-in
- Monitoring dashboards for accuracy, cost, and latency
- Written documentation for your engineering team
- A clear off-ramp if you want to operate it entirely in-house
Why choose us for ai development & integration
We build AI features, not AI strategies
Every engagement starts with an evaluation harness on your real data
Provider-agnostic by default — your code isn't trapped in one vendor
Cost-per-call and accuracy are first-class concerns, not afterthoughts
Same engineering-first process: weekly demos, fixed scope, full handover
AI Development & Integration: frequently asked questions
Often paired with this work
The product the AI feature lives inside.
Learn more →AI Agents & AutomationMulti-step agentic workflows on top of these AI capabilities.
Learn more →Cloud Deployment & DevOpsHosting open-source models and GPU workloads reliably.
Learn more →Mobile App DevelopmentAI features surfaced inside a mobile experience.
Learn more →SEO ServicesContent workflows that benefit from honest AI assistance.
Learn more →Have a specific workflow you want AI to take over?
Send us the inputs, the desired outputs, and the constraints — we'll come back with an honest plan.