01 / GROUND
The model answers from your data, not its memory.
Retrieval, permissions and freshness decided before a model sees a question — so answers cite something real and stay inside what the asker is allowed to see.
Large language models · integrated, not bolted on
LLM integration services for the systems you already run.
Enterprise LLM integration services mean more than an API key. We ground models in your own data, constrain what they can return, measure whether they are getting better, and keep the cost predictable — inside the applications you already depend on.
TRUSTED BY GROWING BUSINESSES
The practical layer
RAG, made operational.
No hand-wavy chatbot experiments. Every component exists to make an answer more useful, safer, or easier to improve.
02 / CONSTRAIN
Output you can act on without reading every word.
Schema-validated responses, refusal paths when the model is unsure, and failure that is loud rather than plausible.
03 / MEASURE
You can tell whether last week's change helped.
An evaluation set built from your real inputs, run on every prompt and model change, so quality is a number rather than an impression.
A disciplined route to value
Make the right thing
before scaling it.
A four-part engagement designed to replace uncertainty with proof, then turn proof into an operating system.
01↗
Find the task
Identify work where language is the bottleneck and the output can be checked.
02↗
Build the eval first
Assemble real inputs with known good outputs before choosing a model.
03↗
Integrate narrowly
Ship one task end to end inside your existing systems rather than a broad pilot.
04↗
Harden and hand over
Budgets, fallbacks, monitoring and a team that can operate it without us.
What we build
Modular services.
One accountable system.
Bring us in for a focused need or keep us close from the first prototype to the millionth query.
RAG and knowledge retrieval
Grounded answers over documents you already own, with permissions respected and every claim traceable to a source.
Extraction and classification
Turn unstructured documents, emails and forms into structured records your existing systems can consume.
Tool use and function calling
Let a model call your APIs safely — with explicit permissions, argument validation and a record of every call.
Evaluation and guardrails
The harness that tells you whether a change helped, plus the boundaries that keep a wrong answer from becoming a wrong action.
Model selection and migration
OpenAI, Anthropic or open source models? We choose against your tasks, not a leaderboard. The provider sits behind an interface, so switching later is a config change.
Cost and latency engineering
Token budgets, caching, precomputation and routing to the cheapest model that clears the quality bar.
Model independence
Pick models for the job,
not for the pitch.
We keep an objective view of the stack: quality, latency, cost and control are all part of the conversation.
How we make choices
Accuracy on your actual tasksEVALUATE
Latency inside your budgetMEASURE
Cost at real input sizesMODEL
Where your data is allowed to goVERIFY
Open weights vs hosted APICOMPARE
Switching cost if you change laterPLAN
Where this work shows up
Document-heavy workflows,
where language is the bottleneck.
The clearest wins are in workflows where people re-key information between systems that will not talk to each other.
Surplus lines insurance is a good example. A submission arrives as an ACORD form — sometimes a clean XML payload, often a scanned PDF or a fax — and has to reach dozens of carriers that each interpret the same nominal standard differently. The work is extraction, normalization and validation before anything is dispatched, and getting it wrong is worse than being slow.
We built that pipeline for EZRater, a comparative rating platform in the US surplus lines market, integrating 40+ carriers whose systems ranged from modern REST to legacy SOAP to flat-file uploads. Read the case study ↗
The same shape recurs elsewhere: claims intake, supplier onboarding, contract review, support triage. Anywhere a person currently reads something and types it somewhere else, an LLM integration has a measurable job to do.
Language models are one part of a wider picture. If the question is broader than text — models, pipelines, real-time inference, legacy systems — start with our AI integration services ↗ and we will point you at the right piece. For workflows that need to act rather than answer, see AI agent development ↗.
Where it works
Built around the
work, not the trend.
We design for environments where context, accountability and speed all matter at the same time.
Insurance
Submission intake, document extraction and cross-carrier data normalization.
Financial services
Grounded internal knowledge with an auditable trail on every answer.
Healthcare
Summarize and retrieve approved information without losing provenance.
B2B software
Embed language features into your product without rebuilding it.
Retail and commerce
Product data enrichment, catalog classification and customer support triage that improves customer experience.
Legal and compliance
Research and review that shows its sources rather than asserting.
Manufacturing
Make manuals, incident history and field notes searchable in context.
Professional services
Turn collective expertise into something a new joiner can query.
Cost
What drives the cost of
LLM integration.
The price of an LLM integration is set by the task, not by the model. These four things decide it, and we scope all four before quoting a fixed price.
How much data needs retrieving
Answering from a few hundred documents is a small project. Thousands of documents with permissions, frequent updates and several formats need a proper retrieval pipeline.
How accurate it must be
A draft that a person reviews can tolerate small errors. An answer that goes straight to a customer needs an evaluation set, guardrails and more testing.
Which systems it connects to
Each system the model reads from or acts on adds integration and permission work, especially older systems without clean APIs.
Running cost per request
Model fees scale with input length and volume. We model them on your real inputs and set per-request limits so the monthly bill is predictable.
Common questions
Frequently asked questions about LLM integration.
Short answers to the questions we are usually asked before an engagement begins.
Connecting a large language model to the systems and data your business already uses, so it can perform a specific task reliably. That means retrieval over your own content, validation of what comes back, permissions, cost controls, monitoring and fallback behavior — not simply calling an API from your application.
OpenAI, Anthropic Claude, Google Gemini and open-weight models such as Llama and Mistral, hosted or self-hosted. We choose against your tasks and constraints rather than a leaderboard, and keep the provider behind an interface so changing later is a configuration change rather than a rewrite.
Usually prompt engineering plus good retrieval is enough. Fine-tuning helps when you need a fixed format or tone at high volume, or a smaller, cheaper model for one narrow task. It does not teach a model facts that change. We fine-tune models only after the evaluation set shows prompting has hit its limit.
Yes. Open-weight models can run inside your own cloud account or on-premises, and the major providers offer regional and zero-retention options. The trade-off is usually capability against control, and it is worth deciding deliberately rather than defaulting either way.
Grounding, constraint and validation. Answers are drawn from retrieved sources rather than model memory and cite what they used; output is validated against a schema before anything downstream consumes it; and the system is built to refuse or escalate when it is uncertain rather than produce a plausible guess.
An evaluation set of real inputs with known good outputs, run on every prompt or model change and compared against the previous version. LLM features degrade silently — the endpoint still returns 200 and the text still reads well — so without a harness nobody notices until a user complains.
Cost scales with input length rather than request count, which makes it harder to predict than conventional infrastructure. We model it against your real inputs during scoping and build ceilings per request and per user, because the usual failure is a retry loop or an oversized context rather than steady growth.
A narrowly scoped integration on one task typically reaches production in weeks rather than months, because the work is bounded by the task rather than the technology. Broad programs take longer, which is why we prefer shipping one task end to end before extending.
No, and we would advise against it. The integration is built alongside what you run, reading and writing through interfaces that already exist. See our AI integration services for how that approach works across the wider stack.
Not through their business APIs by default: data sent through the OpenAI and Anthropic APIs is not used for training unless you opt in. We confirm the retention settings for your account and can use zero-retention or self-hosted options where required.
You do. Source code, prompts, evaluation sets, trained model weights and documentation are handed over and belong to you, and we sign an NDA before any detailed discussion.
A practical first step
Bring us the question that has real stakes.
We'll help you frame the opportunity, identify the constraints and decide what a useful first proof should look like.
READY WHEN YOU ARE
Build with more certainty.
Tell us what your team needs to know, decide or deliver faster.
