Generative AI Consulting
From idea to impact: fast, secure, and scalable AI.
Draft the Q3 renewal note for Northwind.
Drafted from their usage data and last two tickets.
Cite the numbers.
Added three citations, all linked to source records.
Grounded generation with a human in the loop
Capabilities_Matrix
Precision engineering for generative ai consulting demands a specialized toolkit.
Generative AI Consulting
Strategic roadmaps for adopting LLMs and generative agents within your existing enterprise architecture.
Agentic AI Framework Integration
Implementation of advanced agent frameworks like LangChain, CrewAI, and AutoGPT for autonomous workflows.
AI-Driven Automation Solutions
Full-spectrum automation from simple chatbots to complex multi-agent reasoning engines.
Deployment Protocol
Expert guidance on adopting generative AI, building custom LLM applications, and implementing agentic frameworks for enterprise transformation.
Operational Tech_Stack
How the work is sequenced
Grounding, then generation.
The failure mode of generative systems is not bad prose — it is confident, fluent, unsourced wrongness reaching someone who had no way to check it. Everything below is arranged so the system's claims can be traced, measured and constrained before anyone relies on them.
- 01
Establish the source of truth
We identify which content the system may answer from, who owns it, and how current it is. A generative system built over documentation nobody maintains will confidently repeat stale policy, and that problem is editorial rather than technical.
You end up with
A scoped, owned content set with freshness expectations.
- 02
Build retrieval before prompting
Answer quality is governed far more by what the model is given than by how it is asked. We build and measure retrieval first — chunking, ranking and permissions — because a well-phrased prompt over the wrong context is still the wrong answer.
You end up with
A measured retrieval layer that respects existing access control.
- 03
Require citation
Responses cite the passages they rest on, so a reader can verify rather than trust. Where nothing supports an answer, the system says so instead of composing something plausible — refusal is a designed behaviour, not a failure.
You end up with
Cited responses with an explicit and tested refusal path.
- 04
Evaluate against real questions
We evaluate on questions your users actually ask, including the ambiguous, adversarial and out-of-scope ones. Groundedness and citation accuracy are scored alongside helpfulness, because a helpful answer that is not supported is the dangerous case.
You end up with
A scored evaluation suite covering groundedness and refusal.
- 05
Constrain cost and latency
Context length, model choice and caching are engineering decisions with a direct monthly cost. We measure cost per interaction early, because with generative systems adoption is what makes the bill a problem.
You end up with
Cost and latency budgets, measured per interaction.
- 06
Design the review surface
Where output is published, sent or acted on, a human reviews it with the sources visible. The interface is part of the deliverable — review that is inconvenient is review that stops happening within a fortnight.
You end up with
A review interface with sources attached and an audit record.
Where it pays for itself
Generation with a source behind it.
Generative systems do best where the raw material already exists inside the organisation and the work is assembling, adapting or explaining it — not inventing it.
Grounded knowledge assistant
Answering questions across internal documentation with citations and per-user permissions, so people see only what they were already entitled to see.
Tells you it is real
The same questions are asked repeatedly in chat and answered from memory.
Drafting against house standards
Producing first drafts that follow your terminology, tone and regulatory constraints, with the constraints enforced rather than described in a prompt.
Tells you it is real
Reviewers spend more time on consistency than on substance.
Summarisation with traceability
Condensing long correspondence, case files or research where every statement in the summary links back to its source passage.
Tells you it is real
Decisions are made on summaries nobody has time to verify.
Structured extraction
Turning unstructured documents into structured records with confidence scores and the source span attached to every field.
Tells you it is real
Data entry is the bottleneck between receiving and processing.
Code and migration assistance
Translating between frameworks, generating tests against existing behaviour, and documenting systems whose authors have left.
Tells you it is real
A migration is stalled on understanding rather than on writing code.
Multilingual operations
Working across languages while keeping terminology and regulatory phrasing consistent, with review by someone who reads the target language.
Tells you it is real
Translation is outsourced per item with inconsistent terminology.
Where we stop
The limits we design in from the start.
Generative systems are persuasive by construction, which is exactly why the constraints have to be structural rather than advisory.
Unsourced output presented as fact
Where an answer cannot be grounded in a retrievable source, the system declines rather than generating something plausible. Fluency is not evidence, and a system that never says it does not know cannot be trusted when it says it does.
Content that impersonates a real person or organisation
We do not build systems that generate communications, records or reviews presented as coming from someone who did not write them, regardless of the stated purpose.
Training or prompting on data without a lawful basis
Personal data reaching a model is a processing decision with a legal basis behind it, decided before the pipeline is built. Minimisation and retention are architectural, not a policy attestation.
Silent generation in a human channel
Where output reaches a customer, a regulator or an employee, the organisation should be able to say what was machine-generated and who approved it. We build that record in rather than leaving it to convention.
What makes this hard
Everyone has a pilot. Almost nobody has it in production.
Pilots with no path to production
A successful demo in a sandbox rarely survives contact with real data volumes, real permissions and real users.
Confident wrong answers
Fluent output is not correct output, and users trust it more than they should without grounding and citation.
Leaked context
A system that answers over internal knowledge must respect the permissions of the person asking, not the permissions of the index.
Unmanaged model dependency
Building against a single vendor's interface makes a pricing or deprecation decision someone else's to make.
Common questions
Before you
get in touch.
How do you stop it inventing answers?
By grounding responses in retrieved sources, citing them, and designing the system to say it does not know. We evaluate refusal behaviour as carefully as accuracy.
Will our data train someone else's model?
Not under the arrangements we deploy. We configure enterprise terms that exclude training, or run open-weight models inside your own environment.
How do permissions work?
Retrieval is filtered by the asking user's entitlements, so the assistant can never surface something that person could not already open.
Can we switch models later?
That is why we put an abstraction between your application and the provider. Model choice should stay a decision you can revisit as the market moves.
What does this cost to run?
We measure inference cost per interaction during evaluation, so the economics are known before rollout rather than discovered in the first full month.
Ready to integrate?
Begin your transformation with our specialized generative ai consulting engineering team.