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Copilots, assistants, RAG search, and agents — engineered around your data, your guardrails, and the SLAs your business actually cares about.
Most generative AI pilots impress in a demo and disappoint in production. The gap is rarely the model — it is everything around it: retrieval quality, evaluation harnesses, prompt versioning, observability, cost ceilings, and a clear story for when the model is wrong.
We build the whole system. The LLM is one component. The retrieval layer, the eval suite, the human-in-the-loop fallback, and the telemetry that proves it is working are all first-class — from the first sprint, not after the launch goes sideways.
Every engagement leaves your team with working software and the operational assets needed to own it: architecture records, dashboards, runbooks, and handover notes.
Generative AI Solutions roadmap with outcome metrics and assumptions
Architecture decision records and integration contracts
Delivery dashboard covering scope, risks, burn, and demo outcomes
Production code, tests, CI/CD, and environment documentation
Security, accessibility, and performance checklist
Runbooks, handover notes, and operating model recommendations
Week 1
Align on the generative ai solutions outcome, validate constraints, and define the first demo-able slice.
Weeks 2–3
Stand up the delivery environment, agree technical decisions, and ship the first thin slice to staging.
Weeks 4–8
Weekly demos, production-shaped infrastructure, testing, observability, and stakeholder feedback loops.
Launch
Security, performance, accessibility, go-live runbook, and a practical ownership handover.
We make risks visible early: security posture, data migration, accessibility, performance, operational handover, and ownership. The risk register is reviewed in demos alongside working software.
Every project is scoped to its outcome, so these are guides, not quotes. They give you a realistic sense of duration, team shape, and where the value lands.
Validate the outcome, map risks, and leave with a costed plan and a fixed first milestone.
Team: 1 senior engineer + part-time architect
A well-defined product or platform slice delivered to production against agreed milestones.
Team: 2–4 senior engineers + design as needed
A cross-functional team working inside your cadence, owning delivery alongside your people.
Team: Lead, senior engineers, product/design
No exact budget required to start. A 30-minute scoping call turns these shapes into a firm plan and a fixed first milestone.
We connect the engineering constraint to the operational or commercial result it is holding back.
The AI demo never reaches production
A promising prototype stalls because no one can vouch for its accuracy, cost, or safety at scale. The gap between “impressive in a notebook” and “trustworthy in a product” is where most initiatives die.
Hallucinations erode user trust
Without retrieval grounding and evaluation, the model invents answers — and a single confident-but-wrong response can cost you the credibility you spent years building.
No way to measure if a change helped
Prompt and model tweaks are shipped on vibes. With no golden set or graders, every release is a guess and every regression is discovered by a customer.
RAG that actually retrieves
Hybrid search, re-rankers, chunking strategies, and citation-grounded answers — tuned against an evaluation set, not a hunch.
Evaluation from week one
Golden sets, automated graders, and regression dashboards. We can show you whether the prompt change helped or hurt.
Guardrails and safety
Input/output validation, PII redaction, refusal handling, and prompt-injection defenses appropriate to your risk profile.
We would rather show you a result than describe a capability. Here is a recent engagement where this work moved a number that mattered to the business.
Ready when you are
Tell us what you are trying to ship. A senior engineer will follow up within one business day.