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Assistants & knowledge search
Retrieval-grounded assistants that answer from your documented knowledge rather than guessing, with citations back to the source and a clear fallback when the answer is not there.
AI & Intelligent Automation
AI capabilityPractical AI that does real work inside your business.
Assistants, knowledge search, forecasting, classification, document workflows and process automation — built with explicit human controls, defined data boundaries, and an honest view of what the technology can and cannot do.
Overview
AI is only useful when it is pointed at a workflow that actually hurts.
Most AI disappointment starts with the wrong question. Instead of asking what a model can do, we start from the manual burden: the queue nobody wants, the inbox that gets triaged by hand, the report someone rebuilds every Monday.
From there the engineering question becomes tractable. What is the input, who reviews the output, what happens when confidence is low, and how do we know it is working? That framing is what separates an AI pilot that survives contact with production from a demo that quietly gets switched off.
We build AI as part of a system, not as a bolt-on: grounded in your own content, wrapped in permissions, logged for review, and designed so a person stays accountable for the decision.
What you get
- Use-case assessmentA shortlist of candidate workflows scored on value, data readiness, risk and effort — including the ones we recommend you do not automate.
- Working pilotA functioning implementation on your real content and your real workflow, not a sandbox demo.
- Guardrail specificationData boundaries, retention, permissions, human review points, escalation paths and failure behaviour, written down.
- Evaluation harnessA test set and scoring approach so quality can be tracked as prompts, models and content change.
- Operating handbookWho owns the knowledge base, what it costs to run, how to monitor it, and how to roll back.
Scope
What this covers.
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Document workflows
Classification, extraction and summarisation applied to high-volume document processes, with confidence thresholds and a human review queue for anything uncertain.
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Forecasting & planning
Demand, capacity and pipeline forecasting that shows its reasoning, so planners can challenge the model instead of blindly accepting it.
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Process automation
Supervised automation of multi-step internal workflows, with approval gates at the points where a mistake would be expensive.
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Customer-facing assistants
Website and in-product assistants for service questions, lead qualification and appointment support, with a defined handoff to a person.
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Guardrails & evaluation
Prompt and retrieval design, input validation, output constraints, audit logging, and an evaluation set so changes can be measured rather than guessed at.
Where it fits
The problems this actually solves.
If none of these sound familiar, this is probably not the capability you need — and we would rather point you somewhere more useful.
A team answers the same questions dozens of times a week
A knowledge-grounded assistant handles the repeatable answers and routes genuine exceptions to a person, with every answer traceable to a source document.
Documents arrive faster than anyone can read them
Classification and extraction turn unstructured intake into structured records, with a confidence threshold that sends anything ambiguous to a human queue.
Planning runs on a spreadsheet only one person understands
An explainable forecasting model captures the same logic in a reviewable system, with the assumptions visible and adjustable.
Staff copy data between systems to complete a process
Supervised automation performs the mechanical steps and pauses for approval at the decisions that carry real consequences.
Nobody can tell whether the AI feature is actually working
Evaluation sets, logging and review dashboards make quality measurable, so changes can be justified rather than argued about.
Delivery
How we deliver it.
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Frame the workflow
Map the manual process end to end: inputs, decisions, exceptions, who is accountable, and what a good outcome looks like.
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Prove it on real data
Build against your actual content and edge cases. Establish where the approach is strong and where it is not.
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Wrap it in controls
Add permissions, confidence thresholds, human review, logging and the fallback path for low-confidence cases.
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Pilot with real users
Run it alongside the existing process, measure against the evaluation set, and tune based on what users actually do.
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Operate and improve
Monitor quality and cost, expand scope where it earns its place, and retire what does not.
Technology
What we build it with.
Selected around product goals, integration needs, security requirements, team fit and long-term maintainability — not trends alone.
Models & integration
- LLM integrations
- Retrieval-augmented generation
- Embeddings & vector search
- Classification
- Forecasting
Engineering
- Python
- .NET
- Node.js
- PHP
- Laravel
- REST / GraphQL
- Background jobs
- Queues
Data & delivery
- PostgreSQL
- SQL Server
- MongoDB
- AWS
- Azure
- Docker
- CI/CD
Accountability
Every AI feature we build sits inside four controls.
Capability is the easy part. What makes an AI system safe to run in production is the structure around it — and that structure is designed before any model is chosen.
- Your data, bounded. Defined in writing: what leaves your environment, where it goes, how long it is kept.
- Guardrails. Retrieval grounding, input validation, output constraints and confidence thresholds.
- Human review. A person accountable at the points where a wrong answer would be costly.
- Audit log. A record of what was produced, from what source, and what the reviewer decided.
Questions
Straight answers.
That is a configuration and contractual question we settle before any build starts. We document exactly which data leaves your environment, where it goes, how long it is retained, and what the provider's terms allow. If the answer is not acceptable, we change the architecture — self-hosted and in-tenant options exist for most use cases.
Assistants are grounded in your own documented content and cite what they used. When retrieval finds nothing relevant, the correct behaviour is to say so and hand off to a person — and we test for that explicitly rather than assuming it.
Often the most valuable outcome of an assessment is a clear recommendation not to build something. We would rather tell you a workflow is better fixed with an integration or a form redesign than sell you a model that will underperform.
A person, always. We design the review points, the escalation path and the audit trail so that accountability is explicit rather than assumed.
Have a workflow that eats hours every week?
Describe the manual process, who performs it and where it goes wrong. We will tell you honestly whether AI is the right tool — and what a sensible pilot would look like.