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Generative AI Consulting Services
Enterprise-ready AI assistants grounded in your data, safe for regulated environments, and built to stay on budget.
Service
Generative AI Solutions
Best for
Knowledge-heavy teams that spend hours reading, drafting, or triaging unstructured content.
Signature outcome
A bilingual legal assistant trained on a 2-million-document corpus, answering questions with 92% factual accuracy and full citations.
Overview
Generative AI consulting is the work of turning a general-purpose language model into something your business can actually rely on: grounded in your own documents, honest about what it does not know, and safe enough to pass a security review.
We do that by connecting the model to your knowledge base rather than trusting its memory. Answers cite the source document they came from, so a reviewer can check them in seconds. Around that sit the parts that decide whether the project survives contact with your legal and compliance teams: access controls that respect who is allowed to see what, redaction of sensitive fields, evaluation suites that run before every release, and cost dashboards so spend stays predictable as usage grows.
The teams that get the most from this are knowledge-heavy ones, where people spend hours reading, drafting, summarising or triaging unstructured content: legal, policy, bids and proposals, customer support, research and internal help desks. Engagements usually start with a two-week assessment that picks the highest-value use case and states plainly what accuracy is achievable with the data you have today.
How we approach it
Off-the-shelf chatbots hallucinate and leak data. We build AI assistants grounded in your own documents, policies, and systems, with source citations on every answer. Every solution ships with safety guardrails, privacy controls, and continuous accuracy testing, so your legal, security, and compliance teams stay comfortable. Usage and cost dashboards keep spend predictable.
Business questions we answer
“How can we safely put AI to work on our proprietary knowledge at enterprise scale?”
What you get
- AI assistant grounded in your knowledge base with citations
- Privacy, safety, and compliance guardrails
- Continuous accuracy testing before every release
- Usage, cost, and adoption dashboards
- Training and rollout support for end users
Frequently asked
Generative AI Consulting Services: common questions
What does a generative AI consulting engagement include?
A typical engagement starts with a short assessment of your data and use cases, then a scoped build: retrieval over your own content, guardrails, an evaluation suite, and deployment into the tools your team already uses. We hand over documentation and dashboards so your engineers can own it afterwards.
How do you stop a generative AI assistant from hallucinating?
We ground answers in retrieved passages from your own documents and require citations, so unsupported claims are visible. Before release, the assistant is scored against a fixed question set with known correct answers, and we tune retrieval until accuracy clears the agreed bar.
Is our data used to train the model?
No. We use enterprise endpoints with training disabled, and your content stays in retrieval systems you control. Where data residency matters, we deploy inside your cloud tenancy or region.
Should we use a hosted model or an open-source one?
Hosted models win on quality and time to market for most cases. Open-source models make sense when data cannot leave your infrastructure, when volume makes per-token pricing painful, or when you need to fine-tune on proprietary patterns. We benchmark both against your actual task before recommending one.
What does generative AI cost to run?
Running cost is driven by usage volume, retrieved context size and model choice, not by seat count. We model it before the build, then instrument the deployment so you see cost per query and per team, and can trade quality against spend deliberately.
How quickly can we get a working assistant in front of users?
A grounded pilot on a defined document set usually reaches internal users in 4 to 6 weeks. Enterprise rollout, with SSO, permissions and compliance sign-off, typically adds another 4 to 8 weeks.
Proof in production
Relevant case studies
Let’s solve your data challenge