Off-the-shelf LLMs hallucinate, leak, and embarrass you in front of customers. We build custom generative AI — fine-tuned models, RAG pipelines, enterprise chatbots, OpenAI integrations — grounded in your domain and hardened for production.
Generative AI, production-shaped
Your application
chat · copilot · content
Guardrails & retrieval
grounding · validation · PII
Foundation models
GPT · Claude · open-source
live content latency (was 12 min)
domain accuracy on tuned models
ungrounded answers shipped
to a grounded prototype
What we build with generative AI
Each is a deep capability with its own engineering — and a real project behind it. Click in for the detail.
Why ours can be trusted in production
Anyone can wire up a chat box. Making generative AI reliable enough for customers is the engineering that matters.
Retrieval and citation so the model answers from your sources, and says 'I don't know' instead of inventing.
Automated benchmarks plus human review measure accuracy on your domain before anything ships.
Right-sized models, caching and quantization keep token bills and response times where the business needs them.
Generative AI, in production
A sports-media product team had a mandate from a national football league: turn every match event into shareable content, instantly, on-brand. Humans couldn't write a goal post in 8 seconds across every fixture. Guardrailed generative AI could.
PitchSide
Product team, sports-media platform · partner: a national football league
Posts per matchday
Event-to-post latency
Fan engagement
Editor oversees the league
“Our team is great, but no human writes a goal post in eight seconds across every match at once. Now we oversee the whole league from one screen, on-brand, and the engagement numbers tripled. It plugged straight into our stack.”
Straight answers
Fine-tuning shapes behaviour, tone and format; RAG grounds answers in current knowledge. Most production systems use both — a tuned model for how it speaks, retrieval for what it knows. We pick the mix based on your accuracy, freshness, privacy and cost constraints.
OpenAI GPT, Anthropic Claude, Meta Llama, Mistral, and open-source models you can self-host. We choose per use case against your cost, latency, privacy and accuracy needs — and we're not locked to a single vendor.
Retrieval grounding so answers cite real sources, output validators and schema constraints, confidence scoring, and human-in-the-loop review where the stakes justify it. Guardrails are designed in from the first sprint, not bolted on before launch.
No. We deploy inside your environment or a private boundary, with access controls and a clear data-handling policy. Your prompts and documents aren't used to train external models.
A grounded prototype in weeks, not quarters. We ship the smallest version that proves value against a benchmark, then harden and expand — so you see something real early and de-risk the rest.
Tell us the use case. We'll come back within 24 hours with a grounded approach, a benchmark, and a fixed-scope plan.
2000+ vetted engineers · 3 global hubs · 98% client retention
for project discussion
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