Production LLM integration for US teams — past the demo, into the product.
We integrate large language models into US companies’ products and workflows: GPT-4o, Claude, Gemini, and open-source models, with the RAG, caching, fallbacks, observability, and cost controls that separate a reliable feature from an impressive demo. We design around your data sensitivity and the US frameworks your customers expect you to respect.
This page sits inside our broader ai and machine learning service cluster and is designed for teams searching with clear commercial intent.
Who this is for
- US product teams adding AI features that have to work in production, not just demo
- Companies wiring LLMs into existing SaaS, support, or internal workflows
- Teams worried about hallucination, cost, latency, and data privacy at scale
- Operators who want a US-hours partner and full ownership of prompts and pipelines
Service Scope
What we typically deliver
- LLM integration with GPT-4o, Claude, Gemini, and open-source models
- Retrieval-augmented generation (RAG) grounded in your own data
- Caching, streaming, fallbacks, evaluation, and observability
- Cost controls, prompt management, and privacy-first data handling
Delivery Process
How we move from scope to launch
Use-case and data fit
We pressure-test the use case and whether your data is usable, then choose prompt-only, RAG, or fine-tuning based on what the problem actually needs.
Build with guardrails
We ground responses, add refusal patterns and evaluation, and instrument cost and latency from day one — demoed in your US working hours.
Measure and harden
We track hallucination rates, cost per run, and quality, and tune the system for reliable production use as volume grows.
Continue exploring
Portfolio, related services, and ways to connect
AI that earns its place
See how we approach AI and LLM work — grounded, measured, and built for production rather than for a launch video.
AI development approachReal product engineering behind it
LLM work connects to your actual app, data, and auth — backed by our SaaS and web engineering.
SaaS development — USATalk through your use case
Tell us where AI should fit in your product. We will tell you honestly whether it is ready and how we would build it.
Discuss your use caseFrequently asked questions
We design for your data sensitivity. For most US B2B work, the major providers offer enterprise terms that exclude your data from training, with zero-retention options. For sensitive cases we build on open-source models hosted in your own cloud, so data never leaves your environment.
Every LLM carries some hallucination risk. We minimise it with grounding (RAG), refusal patterns so the model says “I don’t know” instead of guessing, and human-in-the-loop for high-stakes decisions — and we measure the rate rather than pretending it is zero.
We route cheap models to easy steps and reserve expensive models for the hard ones, add caching, and instrument cost per run so spend stays predictable as usage grows. We model per-run and per-month cost during scoping.
Yes — live collaboration in overlap with US business hours, USD contracts, and the NDA/MSA structure US companies expect. You own the prompts, pipelines, and any fine-tuned weights.