Rented intelligence carries a bill that grows with your success and a memory you cannot audit. We build large language model systems trained on your domain and running in your environment, and when the engagement ends, the weights, the evals, and the prompts leave with you.
Scoping that starts from the pilot you already have and names exactly what stands between it and production traffic.
Our engineers build language model systems end to end, from base model selection across GPT, Claude, Llama, Mistral, and Gemini to the application layer your users touch.
LLM integration connects the model to your CRM, support desk, and internal tools through production APIs, so answers appear where your teams already work; delivered through our AI integration work.
Domain adaptation through SFT, DPO, and parameter-efficient methods like LoRA and QLoRA, so the model answers in your terminology and to your standards.
When answers must come from a living document base rather than frozen weights, retrieval is the cheaper and more auditable path; our RAG development services cover it end to end.
Serving architecture on AWS, Azure, or GCP designed for workloads that are bursty and long-running, not for the stateless traffic your current autoscaling assumes.
One provider outage should degrade your system, never take it down; the gateway is where that guarantee lives.
Standard APM tells you a request took nine seconds; it cannot tell you which reasoning step, tool call, or retrieved document produced the wrong answer.
The first output is an audit of the system you already have: where it breaks under load, what happens when the provider fails, what an uncapped month costs, and what cannot be debugged today. Each gap arrives priced and ranked, so the plan starts from evidence.
Next comes the target design on paper: serving setup, gateway, queueing, and the projected cost per query at your actual concurrency. This is the document your CFO reads before any build spend starts.
The working build arrives here: infrastructure stood up, failover routing live, guardrails at the boundary, cost caps enforced in code. Load and failure testing runs through our AI-powered testing services in a staged environment, so the weak points surface before launch.
Before anything faces traffic, the full evaluation harness lands in your hands: task benchmarks, adversarial tests, regression checks. Passing thresholds get signed off by your team.
Rollout moves behind feature flags and canary deploys, with exposure growing only as accuracy and latency hold at each step. Every prompt and response gets logged for audit from the first real user.
After launch, dashboards track accuracy, spend, and latency against the agreed thresholds, with alerts and retraining triggers your engineers own. The monitoring pipelines come from our MLOps services practice and run in your environment from day one.
5 Claude Certified Architects to join your project now, so you get the frontier-model expertise in-house.
92% senior talent across the team, people who have carried models through production incidents.
99% on-time delivery across fixed-scope engagements, with managers running the cadence.
ISO 27001:2022 certified, HIPAA-ready, GDPR covered. Your data trains your model and nobody else’s.
The Relevant AI Delivery Framework™ pairs AI-assisted development with senior review at every gate: up to 50% faster delivery and up to 20% fewer bugs in production.
96% employee retention keeps the people who know your architecture on your project.
A readiness read prices them for your system: where it breaks under load, what an uncapped month costs, what cannot be debugged today. The fixes we build all transfer to you.
A pilot that answers well in front of five users proves the model fits the task. It proves nothing about the system around the model, and that system is what production tests.
Three checks give the answer before engaging anyone:
If you want a structured answer across the team, data, and processes as well, an AI maturity assessment maps all of it before any build spend starts.
Every production response needs its chain recorded, the prompt version, the retrieved documents, each model call, every intermediate step. With that in place, debugging becomes a lookup: find the request, see which link in the chain diverged, fix that link.
Standard APM will not close the gap, because it shows a slow request and cannot show a wrong one. A test for your current setup: take the last answer a user flagged and ask your team to name which retrieved document fed it. If the answer takes more than an hour, tracing is the first gap to close, and it ships in the first sprint of every build here for exactly that reason.
Yes, and inherited pilots are a growing share of the work: systems built quickly with AI-assisted tools tend to arrive with solid ideas and fragile plumbing.
The engagement starts with the same production readiness read as any project, so the gap list and its cost are on the table before any build commitment. Where the codebase itself needs stabilizing before infrastructure work makes sense, our vibe coding cleanup services team takes that stage first. Nothing gets rebuilt for the sake of rebuilding: the audit separates what holds from what breaks, and you keep the audit either way.
The budget is set by three variables, and knowing them puts you in control of the quote before anyone sends one:
A useful exercise before requesting any proposal: write down your peak concurrent users, average tokens per query, and the compliance frameworks you answer to. Those three numbers turn a vague estimate into a defensible one.
Both models run in production today, and the right one depends on where your bottleneck sits.
When your engineers know the domain but the LLM infrastructure expertise is missing, individual specialists slot into your team: a gateway engineer, an observability engineer, working in your repository and your standups.
When the whole production layer needs to be built while your team keeps shipping the product, a dedicated team takes the workstream end to end and hands it over running.
A signal for choosing: count how many of the four production gaps on this page your team could close alone given time. Two or more, and you need specialists to fill holes; fewer, and a separate workstream will move faster than stretching your roadmap.
Everything transfers, in the fullest sense the licensing allows: infrastructure code, gateway and observability configuration, fine-tuned weights or adapters, the eval suite, and the prompt library, with runbooks your engineers operate without outside help.
Where the build sits on a proprietary base model, you own everything layered on top plus a written exit path to an open-weights alternative, so no single vendor holds your roadmap.
A clause worth demanding from any partner: the contract should name the artifacts that transfer and the format they arrive in, because ownership that lives in a slide deck is not ownership.
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