The demo was never the hard part
A generative AI prototype takes days. What takes months is everything after: knowing when the model is wrong, keeping answers grounded in your own content, controlling what it is allowed to do unsupervised, and proving to a reviewer that it behaved.
We treat that second half as the actual engagement, and the prototype as the easy first week. It is also why most of what follows is about evaluation and boundaries rather than model choice.
Where generative AI consulting usually earns its fee
Four of these are about reliability rather than capability. That reflects where production projects actually fail.
LLM applications
Drafting, summarising, extraction, classification, and structured output against your own schemas. Built with the failure cases mapped first, because a model that is right most of the time is a support burden if nobody knows which times.
Retrieval and RAG
Grounding answers in your content with source citation and permission awareness. Chunking, retrieval tuning, and the grounding checks that stop a confident answer being assembled from the wrong document.
Agents and tool use
Systems that take actions, not just produce text. Scoped tool access, bounded autonomy per step, and an audit trail of what was called with which inputs.
Evaluation harnesses
A test suite for a system with no single correct answer. Graded cases, regression runs on every change, and thresholds agreed before launch rather than argued about after an incident.
Guardrails and human review
Where the system acts alone, where it drafts for a person, and where it refuses. Review queues for low-confidence cases, and explicit behaviour for the inputs it should not answer.
Monitoring and cost control
Quality, latency, and spend tracked per feature after launch. Model behaviour changes under the same endpoint name, so drift detection is part of the build rather than a later discovery.
Three engagements, published starting figures
Most generative AI work starts with a Discovery Sprint, because a build estimate is only honest once the data and integrations are understood.
Two weeks. We map the workflow, assess data access and integration reality, define what good output looks like, and score the candidates. You leave with an architecture document, evaluation criteria, and a costed roadmap, whether or not you build with us.
A dedicated team builds the system with an evaluation harness from week one. By the end of week two you see your own workflow running on your own data. If it is not what you wanted, either side can stop there and we refund the unused balance.
For generative AI you already have. We review evaluation coverage, grounding, prompt and tool-access security, deployment, and observability, then fix the critical gaps inside the engagement.
You get the evaluation suite, the monitoring, and the documentation. The measure of a good handover is that your team can change the system without calling us, and we would rather be re-hired than retained by default.
Deliverables, not a slide deck
Every engagement produces artifacts your team keeps and can inspect.
What is different about how we deliver
Mostly that the unglamorous half is already built.
The platform already exists
Bureau is the platform behind every Clarvia engagement. Auth, roles, audit trails, and cost tracking are in place before your project starts, so you are not paying us to rebuild infrastructure that has nothing to do with your problem.
Evaluation from week one
The evaluation harness is built alongside the feature, not bolted on when quality becomes a complaint. It is what makes the difference between shipping a change confidently and hoping.
Bounded autonomy, stated explicitly
We design autonomy step by step. Where it is safe, the system acts. Where the stakes are real, a person reviews. The boundaries are written down and testable rather than implied.
Published starting figures
Our pricing page carries starting figures for all three engagements, what moves the number, and the work we decline. You can estimate roughly where you would land before speaking to us.
Where to look next
Deeper detail on the engagements and approach behind this work.
Common questions
What are generative AI consulting services?
Help getting a generative AI system from an idea into production and keeping it reliable there. In practice that means choosing an architecture, grounding output in your own content, deciding what the system may do unsupervised, building the evaluation that tells you when it is wrong, and putting monitoring behind it. The model is rarely the hard part.
How is this different from hiring an LLM developer?
A developer builds the feature. A consulting engagement also decides whether the feature should exist, what acceptable output is, where a human belongs in the loop, and how you will know months later that behaviour has drifted. If you already have those answers and just need capacity, hiring is cheaper and we will say so.
Do we need RAG, fine-tuning, or agents?
Usually retrieval first. RAG grounds answers in your content and is easier to inspect and correct than a fine-tuned model. Fine-tuning earns its place for format and tone consistency at volume. Agents are worth it when the system must take actions rather than produce text, and they raise the bar on access control and audit.
How do you stop it making things up?
Grounding and evaluation, not prompt wording alone. Answers are retrieved from your content with citations a reviewer can follow, low-confidence cases route to a person, and graded test cases run on every change so a regression shows up before your users find it. No system is perfect, so the design assumes wrong answers and constrains what they can cost.
Can you work with our existing model provider or cloud?
Yes. We are vendor-neutral by design and have no partnership quotas to fill. Model and provider are chosen against your constraints on latency, cost, data residency, and procurement. Being able to move providers later is treated as a design requirement, since behaviour changes under the same endpoint name.
Do you publish pricing?
Yes. Starting figures for all three engagements are published on our pricing page, together with what moves the number. Scope still drives the final cost, so a fixed figure is agreed in writing against the agreed scope before build work starts.
What if our data is not ready?
That is the most common finding in a Discovery Sprint, and it is better to find it in week one than in month three. We assess data access, quality, and permission structure early. Sometimes the honest recommendation is to fix the data before building anything generative on top of it.
Tell us what you want the system to do
Book a free 15-minute triage. We will tell you whether generative AI is the right tool for it, which engagement fits, and roughly what it costs.