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LLM integration
GPT-4, Claude, Gemini or open-source models embedded into your product — chatbots, summarization, content generation, coding assistants.
AvailableTaking new engagements · Q2 2026
All servicesService · Intelligence · 02
AI features your competitorscan't copy overnight.
Give your product an unfair advantage. We build intelligent applications that automate work, predict outcomes and learn from data — wired in cleanly, not bolted on as a demo.
- GPT/Claude
- Frontier models
- RAG
- Retrieval pipelines
- MLOps
- End to end
- SOC 2
- Privacy-ready
What we build · 06 capabilities
AI that actually solves problems.
Not chatbots-for-the-sake-of-it. AI features tied to a measurable outcome, instrumented from day one.
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RAG & knowledge agents
Retrieval-augmented agents that answer from your own data — internal docs, support history, product catalogs — with citations and freshness.
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Predictive analytics
Forecasting, anomaly detection and recommendation models that turn historical data into decisions you can act on today.
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Custom ML models
Models trained on your specific data for classification, ranking, fraud detection or any domain-specific problem — versioned and observable.
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AI-powered APIs
Endpoints that understand natural language, extract entities, classify content and return structured data your product can act on.
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Secure & compliant
Encryption, access controls, data residency and compliance with GDPR, HIPAA and SOC 2. Your data never trains anyone else's model.
How we work · 04 stages
From hypothesis to production, with guardrails.
- Stage / 01
Frame the problem
We define the user task, the success metric and the evaluation set before touching a model. No vibes-based AI features.
- Stage / 02
Prototype on cheap models
We start with the cheapest model that could work. If a small model fails, we know exactly what scale buys us.
- Stage / 03
Ship + instrument
Evals, tracing, prompt versioning and feedback loops live before launch — not bolted on after a regression.
- Stage / 04
Iterate on real traffic
Weekly model and prompt tweaks driven by production data. We track quality and cost, never just one or the other.
AI stack we use
Frontier where it matters, boring where it doesn't.
- OpenAI
- Anthropic Claude
- Google Gemini
- Llama
- Mistral
- LangChain
- LlamaIndex
- Pinecone
- pgvector
- Weaviate
- Hugging Face
- PyTorch
- TensorFlow
- Vercel AI SDK
- LiteLLM
- Langfuse
Why teams pick us
AI features that ship, and stay shipped.
Most AI demos die in production. Ours don't, because we treat evals, observability and cost as first-class engineering — not afterthoughts.
- i.
Eval-first
Every AI feature ships with an eval set. Quality is measured, not asserted in a demo.
- ii.
Cost-aware
We track $/request from day one and route between models to keep your unit economics healthy.
- iii.
Your data, your model
Strict data isolation. Your training data never leaks to a shared pool. Privacy is the default, not an upsell.
Common questions
What teams ask before they buy.
No. We use enterprise/zero-retention endpoints and configure data-handling policies so your data is not used for model training. We document this in writing.