Turn Generative AI Into A Business Advantage.
A public chatbot knows the internet. It does not know your pricing rules, your contract history or why last quarter went the way it did. Generative AI becomes an advantage only when it is grounded in your own material, restricted to what each person is allowed to see, and measured against answers you already know to be right. That is the system we build.
- Your data, your infrastructure
- Retrieval with citations
- Quality measured, not assumed
Five layers between your files and a useful answer
Every generative AI system we build has the same spine. The technology inside each layer changes with the project; the sequence does not.
Generative AI architecture
data flowingSelect any layer to see what happens there. Most generative AI projects fail at the knowledge layer, not the model layer — which is where the majority of our build time goes.
Ten generative AI systems that pay for themselves
All of them share the same foundation: your content, retrieved properly, used by a model that has been told exactly what it may and may not do.
Custom AI assistants
An assistant that knows your products, your policies and your customers, built for one job rather than a general chat window with your logo on it.
Private AI knowledge bases
Your documents indexed in infrastructure you control, so staff can ask a question in plain language instead of hunting through a shared drive.
RAG systems
Retrieval-augmented generation: the model answers from passages retrieved out of your content, with the source attached to every claim.
Document generation
Proposals, quotations, reports and contracts assembled from your approved templates, your pricing rules and the specifics of the case in front of you.
AI content systems
Content pipelines with your brief, tone and factual constraints built in, plus a review step — not a button that produces a thousand unusable articles.
AI research
Agents that gather information across sources and return a structured, sourced brief in the format your team already reads, on a schedule or on demand.
AI summarisation
Long calls, threads, tenders and reports reduced to the decisions, the numbers and the open questions, with a link back to the passage each point came from.
Enterprise search
One question box across systems that were never designed to talk to each other, returning answers that respect each system’s existing access rules.
Custom LLM applications
Purpose-built software where a language model is one component alongside your data, your business logic and your interface — not the whole product.
AI-powered workflows
Generation embedded inside a process that already runs: draft the reply, classify the ticket, extract the fields, then pass it on for approval.
Your data stays yours
This is the question that stops most generative AI projects, and it deserves a specific answer rather than a reassuring one.
We do not publish blanket security guarantees on a marketing page, and we would treat any vendor who does with suspicion. What we do instead is document, before you commit to anything: where each piece of data sits, which third parties process it, who can retrieve it, how long it is kept, and what happens to it if you end the engagement.
Private knowledge bases
Your content is indexed into storage you own or control — your cloud account, your database, your vector index. We do not pool your material with anyone else’s, and you can export or delete the whole index without asking us.
Permission-aware retrieval
Retrieval respects the access rules your source systems already enforce. A passage from an HR file does not become readable simply because it was indexed. Permissions are attached at indexing time and checked again at query time.
No training on your data by default
We do not use your content to train models, and we configure model providers so your inputs are not used for their training either. Provider terms differ and they change, so we put the specific terms in front of you in writing rather than offering a blanket promise.
Deployment options
Managed cloud, your own cloud tenancy, or open-weight models self-hosted on hardware you control. Each option trades cost, answer quality and control differently. We set out that trade-off honestly instead of defaulting to whichever is simplest for us.
Where generative AI earns its keep — and where it does not
Half of our value in the first two weeks is telling clients which of their ideas we would not build. A model applied to the wrong problem is an expensive way to be wrong faster.
Good fits
Questions whose answer already exists in your documents but nobody can find it in time
First drafts of repetitive written work — proposals, replies, summaries, handover notes
Classifying and routing unstructured input such as emails, tickets and inbound forms
Condensing long material where the reader can still open the source and verify it
Extracting structured fields from messy documents, paired with a human review queue
Search across systems that were never designed to talk to each other
Poor fits
Exact arithmetic without tools — calculation belongs in code the model calls, with the working shown
Hard compliance or regulatory decisions with no qualified human reviewing the output
Anything that needs one guaranteed correct answer and has no verification step attached
Work where the source information is contradictory or missing — a model cannot fix a data problem
Decisions under a tight real-time latency budget, where generation is simply too slow
Tasks where the cost of a single wrong answer exceeds the cost of a person doing it properly
Seven steps between a question and a defensible answer
The permission check happens before retrieval, not after generation. That ordering is the difference between a system your legal team approves and one they quietly shut down.
Grounded generation
live workflow- Question asked
- Permission check
- Retrieve
- Ground
- Generate
- Cite
- Deliver
If retrieval returns nothing the person is entitled to see, the chain stops before generation and the system says so. Silence is a better answer than a plausible invention.
The technical questions worth asking
If a supplier cannot answer these five clearly, they are reselling an API rather than engineering a system.
Whichever one fits the task, the budget and the data constraints — and usually more than one in the same system. Routine classification and extraction run well on small, cheap models. Nuanced drafting and multi-step reasoning need a frontier model. Sensitive material that cannot leave your infrastructure points to a self-hosted open-weight model. We build the model choice as a configuration rather than a hard dependency, because the leading option changes every few months and you should be able to move without a rebuild.
Point generative AI at something that matters
Bring one process where the answer already exists inside your business but takes too long to find. We will show you what a grounded system would do with it, and what it would cost to run.
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