P/01Product concept
AI Agent Workspace
A governed workspace where AI agents take on multi-step work with people approving what matters.
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AI products and custom AI engineering
Intelligence engineered for the way your business works.
We design, build and operate AI products and custom AI systems that fit real workflows, with evaluation, governance and human oversight built in from the first sprint.
/ 02Featured products
Six product families,
labelled honestly
Packaged AI capabilities for the problems we see most often. Each is a product concept today, delivered as a configured build through our engineering practice. We say so on every page.
P/02Product concept
Enterprise Knowledge Copilot
Grounded answers from your own documents and systems, with sources, permissions and clear uncertainty.
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P/03Product concept
Document Intelligence
Classification and field extraction for contracts, invoices and forms, with validation and human review.
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P/04Product concept
Conversational & Voice AI
Chat and voice assistants that resolve routine requests and hand over cleanly to people.
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P/05Product concept
Workflow Automation Hub
AI steps inside dependable workflows: triggers, rules, models and approvals in one orchestrated flow.
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P/06Product concept
AI Analytics Copilot
Ask questions of governed business data in plain language and see the query behind every answer.
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/ 03Engineering services
Advise. Build.
Integrate. Operate.
Custom AI engineering for problems that do not fit a product. Four pillars, one team, from the first readiness question to the system running reliably in production.
01 / Advise
Strategy, readiness, use-case selection and governance, so investment goes to problems AI can actually solve.
Services
Typical deliverables
- Readiness assessment
- Scored use-case portfolio
- ROI model
- Governance framework
/ 04Solutions by outcome
From business input to governed outcome
Every solution follows the same shape: your signals come in, AI orchestration interprets them, and outputs pass the controls you define, including human approval where it matters.
InputsOrchestrationApproved outputs
7 use cases shown
- Customer Support & CXFaster, more consistent service: AI triage, agent assist and self-service that hands over cleanly.
- Sales & Lead OperationsCleaner pipelines and better-prepared sellers: enrichment, routing, research and follow-up drafting.
- Internal Knowledge SearchAnswers from policies, documentation and past work, with citations and permissions intact.
- Document & Back-office AIClassify, extract and validate document data, and route exceptions to the right people.
- Finance & ComplianceReconciliation support, policy checks and evidence gathering, with controllers firmly in charge.
- HR & People OperationsPolicy answers, onboarding support and document handling, designed with privacy and fairness in mind.
- IT & EngineeringService desk automation, incident assistance and engineering knowledge, connected to your tooling.
/ 05Proof and trust
Evidence before
adjectives
We publish client results only with client approval and evidence. Until then, we show you exactly how we would build: reference architectures, clearly labelled as illustrative, and the practices we apply to every system.
Illustrative architecture
Permission-aware knowledge copilot for a multi-site operations team
- Sources Document management system with site-level permissions
- Ingestion Structure-aware chunking by heading and table
- Retrieval Hybrid keyword and vector search
- Generation Answer only from retrieved passages
Illustrative architecture
Contract clause extraction to support an in-house legal team
- Intake Contract repository export
- Extraction Clause location with page references
- Validation Cross-check against amendment documents
- Review Side-by-side source and field view
Illustrative architecture
Agentic triage and reply drafting for a B2B support queue
- Trigger New ticket webhook
- Agent tools Read account plan and usage
- Reasoning Plan, gather, propose loop with step limit
- Human control Agent edits and sends
Human oversight by design
Consequential actions such as sending, writing to systems of record or spending require a named person to approve, with the evidence in front of them.
Evaluation before release
Every AI system ships with a test set agreed with domain experts. Changes to prompts, models or retrieval are scored before they reach users.
Least privilege
AI components receive only the data and tool permissions a task needs. Secrets stay server-side and are never placed in prompts.
Your data stays yours
We use model providers under terms that exclude training on your data, or host models in your environment. No client data is used to train shared models.
Traceable outputs
Answers cite their sources. Runs are logged with redaction and retention you control, so any output can be investigated.
Deployment where you need it
Commercial APIs, regional cloud services or private hosting, chosen per workload against your data protection obligations.
/ 06Delivery process
Discover, build, deploy, run
A delivery rhythm designed to surface risk early, prove value with measurements and leave you in control of what we build.
Weeks 1 to 4
Discover
Real questions, real data samples and a measured baseline. We agree the scorecard and decide go or no-go on evidence.
- Risk
- Kills weak ideas cheaply
- Controls
- Data handling agreement, risk tier
Iterative sprints
Design & Build
Narrow first slice in production-grade code. Every sprint ends with a demo and evaluation results against the agreed set.
- Risk
- Quality measured, not assumed
- Controls
- Threat model, evaluation suite, approvals
Controlled rollout
Deploy
Shadow mode, then a pilot group, then wider release. Monitoring, guardrails and rollback are in place before users arrive.
- Risk
- No big-bang launches
- Controls
- Access controls, logging, incident runbook
Ongoing
Run
We operate, improve or hand over. Documentation, runbooks and knowledge transfer are deliverables, not afterthoughts.
- Risk
- No lock-in by obscurity
- Controls
- Upgrade testing, cost and quality reports
/ 07Insights
Research you
can use
Practical writing on agents, retrieval, evaluation and governance, with sources for every technical claim.
Agentic AI
What makes an AI agent reliable in production
Reliable agents come from narrow scope, constrained tools, explicit stopping rules and continuous evaluation, not from a more capable model alone.
3 min read
Knowledge AI
When retrieval-augmented generation works, and when it does not
RAG succeeds or fails on retrieval quality. A guide to where it fits, the common failure points and how to measure each stage separately.
2 min read
Document AI
How to evaluate document extraction before you automate
Field-level precision and recall, confidence calibration and straight-through thresholds: a practical method for judging document AI on your own files.
2 min read
/ 08Start a conversation
Bring us the workflow.
We will bring the evidence.
Choose the route that matches where you are. Each one reaches an engineer, not a sales script.
01Book an AI ConsultationYou have a problem or idea and want an expert view on whether and how AI fits.Continue 02Request Product DemoYou want to see a product concept walked through against your workflow.Continue 03Get an AI Project ProposalYou know the scope and want a costed, phased proposal.Continue