Knowledge trapped in documents
Answers exist somewhere in shared drives and wikis, but finding them takes too long.
Generative AI development
Generative AI grounded in your knowledge — not the open internet
We build generative AI systems that draft, summarize, answer and explain using your approved documents and data. Outputs are grounded, cited and reviewed where it matters, so your team can trust what the system produces.
Overview
What generative AI can do for your business
Generative AI is most useful when it is connected to your own knowledge: policies, product documentation, past proposals, contracts and customer history. Without that grounding, answers are generic or wrong.
We design retrieval, prompts, guardrails and review steps together, then measure output quality against examples your experts approve.
Problems we solve
Sound familiar?
Repetitive drafting
Proposals, reports, responses and summaries are written from scratch with the same building blocks.
Unreliable public AI tools
Staff paste company data into consumer tools and get confident but unverified answers.
How it works
An example, step by step
A typical engagement shown as a single run from trigger to measurable outcome. Every step, tool and checkpoint is tailored after consulting and audit.
- 01 · Business triggerRequest for proposal received
- 02 · AI agentProposal drafting assistant
- 03 · ReasoningMatches requirements to past proposals, case notes and service descriptions
- 04 · Business toolsDocument library · CRM · pricing sheet
- 05 · Automated actionProduces a structured first draft with sources for each section
- 06 · OutcomeYour team edits and approves instead of starting from a blank page
What you get
What we deliver
Knowledge assistants
Chat and search over your documents, respecting who is allowed to see what.
Drafting systems
First drafts of proposals, reports and responses assembled from approved content.
Summarization pipelines
Consistent summaries of calls, documents and threads delivered to the right place.
Content governance
Style, terminology and compliance rules enforced through prompts, checks and review.
Quality evaluation
Scored test sets for accuracy, grounding and tone, rerun on every change.
Private deployment options
Configurations that meet your data-retention and residency requirements.
How we engage
Consulting first. Then delivery.
Every engagement follows the same disciplined path, so scope, risk and success measures are agreed before anything is built or taught.
Consultation
We understand your goals, constraints and context — and tell you honestly whether this service is the right fit.
Discovery & scoping
We map the relevant processes, data, systems and people, and agree scope, risks and success measures in writing.
Implementation sprints
Focused sprints with weekly demos, tested on your real examples before go-live.
Training & handover
Your team is trained to use, supervise and improve the result, with full documentation.
Measure & optimize
We compare results against the baseline and agree the next priority.
Use cases
Where this works well
- Internal policy and SOP assistant
- RFP and proposal first drafts
- Customer-facing help assistant
- Meeting and call summaries
- Product description generation
- Contract clause comparison
Technology
Tools and platforms
- Retrieval-augmented generation (RAG)
- Embeddings and vector search
- Leading LLM providers
- Guardrails and output validation
- SharePoint, Google Drive, Notion, Confluence connectors
We’re tool-agnostic and recommend what fits your volume, security needs and team. See integrations.
Related
Industries and insights
Professional Services
Consultancies, agencies, legal, accounting and advisory firms sell expertise — yet a large share of every week goes to intake, document handling, status updates and reporting.
Learn moreHealthcare Operations
Clinics, practices and health-services businesses carry a heavy administrative load.
Learn moreReal Estate
In real estate, the fastest reply often wins and every transaction involves a trail of documents and deadlines.
Learn moreFAQ
Generative AI: common questions
What is retrieval-augmented generation (RAG)?
RAG retrieves relevant passages from your own documents and gives them to the model when it writes an answer. It keeps answers grounded in your content and lets the system cite its sources.
Will our data be used to train public models?
We configure providers and plans that do not train on your inputs where available, and document exactly where data is processed and stored.
How do you reduce hallucinations?
Grounding in retrieved sources, instructions to answer only from those sources, citation requirements, automated evaluation, and human review for high-stakes outputs.
Can it follow our brand voice?
Yes. We encode tone, terminology and formatting rules and test outputs against approved examples.
Talk to us about generative AI
Book a free 30-minute strategy call. We’ll look at one or two of your processes, tell you honestly whether AI is the right fit, and outline what a first project could look like.
- 30 minutes, no obligation
- Honest fit assessment
- Clear next step