Bolting an LLM onto a product is easy to demo and hard to ship. We integrate OpenAI and Claude into your systems the way they survive real use — grounded on your data, guarded against hallucination, observable in production.
The model answers from your documents and data, with citations — not from its training set. Retrieval is the difference between useful and plausible.
The same zero-hallucination discipline behind DataGreat: grounded retrieval, guardrails, and human review where the cost of being wrong is real.
Logging, evals and cost controls from day one. You can see what the model was asked, what it retrieved, and what it answered — and roll back a bad prompt.
Most 'AI integration' stops at an API key and a prompt. The work that matters is retrieval, grounding, evaluation and the plumbing that keeps it reliable under load.
Retrieval-augmented generation over your documents, tickets, catalogue or knowledge base. Chunking, embeddings, reranking and citation — built to answer from your data.
OpenAI, Claude and open-weight models wired into your product and back office, behind an abstraction so you are never locked to one vendor.
Purpose-built assistants grounded on your content, with the tools and permissions your workflow needs — internal or customer-facing.
Input and output validation, refusal handling, and grounding checks so the system says 'I don't know' instead of inventing. This is the part demos skip.
Eval sets, regression checks and production logging so you know when a prompt or model change made things worse before your users do.
When a single call isn't enough: multi-step agents that call tools, check results and act — the same discipline behind Betty and Sentinel AI.
We treat an AI feature like any other production system: measured, grounded, and observable — not shipped on vibes.
What exactly should the model do, on what data, and how will we know it's right? We write the eval set before we write the prompt.
≈ 7 daysBuild the retrieval layer over your real data, so the model answers from your source of truth. Grounding first, generation second.
≈ 14 daysAdd guardrails, run the evals, and measure against the bar. Failures here are cheap; failures in production are not.
≈ 14 daysDeploy behind logging and cost controls, watch real usage, and iterate on the prompts and retrieval with data instead of guesses.
When a wrong answer costs a customer, a contract or a compliance breach, plausible isn't good enough — the integration has to be grounded and measured.
Zero-hallucination isn't a slogan here — it's the pipeline behind a product serving verified data for 42 countries.
Verified travel intelligence for 42 countries, enriched by a purpose-built pipeline that grounds every output and flags anything it cannot source. The reference for how we integrate AI: grounded, cited, reviewed.
An autonomous pentest agent integrated with the Kali toolchain: it calls real tools, interprets output and acts. Proof we ship agentic AI that operates against live systems, not toy demos.
A production agent with 24 tools across 8 categories, driven over WhatsApp inside a live business. Real LLM integration under real operational load.
We start by defining the task and the success bar. If an LLM is the wrong tool for it, we'll tell you before you spend on a retainer.
You have one AI feature to get right.
You have a roadmap of AI features.
AI is core to the product and needs depth.
Vendor-flexible at the model layer, rigorous everywhere the reliability lives.
What people ask before commissioning an AI integration.
AI integration services connect large language models — OpenAI, Claude, open-weight models — into your product and workflows so they do real work: answering from your data, automating a task, powering a feature. The hard part isn't the API call; it's grounding, guardrails and observability that keep it reliable in production.
RAG (retrieval-augmented generation) makes the model answer from your documents and data instead of its training set, with citations. If your use case involves your own content, catalogue or knowledge base — and accuracy matters — you almost certainly need it. It's the single biggest lever against hallucination.
You ground it and you measure it. Retrieval over your source of truth, guardrails that validate output, and a design that lets the system say 'I don't know' rather than invent. Then an eval set catches regressions. It's the same discipline behind DataGreat's zero-hallucination pipeline.
No. We put a model-agnostic abstraction between your product and the provider, so switching between OpenAI, Claude or an open-weight model is a config change, not a rewrite. Vendor lock-in is a choice, and we design against it.
Yes — most of our integration work lands inside an existing codebase and existing systems. We wire retrieval and models into what you already run, behind logging and cost controls, rather than asking you to adopt a separate platform.
Because we run grounded AI in production. DataGreat serves verified data for 42 countries on a zero-hallucination pipeline, and Betty and Sentinel AI are agents that act on real systems. We integrate AI the way we already run it — measured, grounded, observable.
AI integration rarely ships alone. These are the tracks it usually touches.
Tell us the feature or workflow you want AI in. We'll define the task, the data and the success bar — and tell you honestly whether an LLM is the right tool.
