We integrate AI into the tools your team already uses — support, sales, documents and back-office — with guardrails, evaluation and a written scope.
If any of these sound familiar, there is a system worth building — and it does not start with a chatbot.
The same twenty questions arrive every day, and every answer needs a person who already knows the product.
Leads arrive unenriched, half-qualified, and get routed by whoever checks the inbox first.
Invoices, contracts and forms arrive as PDFs and end up as manual data entry into a system that should already know.
The answer exists somewhere — in a policy document, a past ticket, a Slack thread nobody can find.
Copy from email, paste into the CRM, update the sheet, notify the channel. Every time, by a person.
Numbers live in three systems, so a monthly view needs a human to reconcile them by hand.
Each one is a production system with logging, guardrails and a human fallback — not a prototype left in a notebook.
A private assistant that answers from your own documents, tickets and databases — with citations, not guesses.
Answers routine questions in your support tool, then hands over to a human with full context when confidence drops.
Extract, validate and push structured data from invoices, contracts and forms into the systems that need it.
Multi-step processes wired together: enrich, decide, write, notify. Deterministic where it matters, AI where it helps.
Score and route inbound leads, draft the first reply, and keep the CRM accurate without a human doing data entry.
Answers common calls, books appointments and captures details outside business hours — with a clean escalation path.
Need something that is not listed? Describe the workflow — the system is designed around it.
Ten sectors where the same patterns repeat: high enquiry volume, document-heavy processes and knowledge that lives in people's heads.
Product Q&A, order status, returns triage and catalogue enrichment.
Listing enquiries, viewing scheduling and document collection.
Appointment handling, intake forms and policy questions — with strict access control.
Contract review support, clause search and matter intake.
Invoice capture, reconciliation support and client query handling.
Exception triage, document processing and status communication.
Admissions enquiries, course matching and student support.
Spec and manual search, quote preparation and maintenance knowledge.
Booking changes, itinerary questions and multilingual support.
Onboarding help, ticket deflection and internal engineering knowledge.
Dedicated industry pages are being prepared. Until then, ask about yours directly.
Three reference engagements: the problem, the system we would build, and what changes for the team.
Support
Problem
A 4,000-document help centre and a ticket queue with a two-day first response.
System
Retrieval assistant over the help centre and resolved tickets, answering in the support tool with citations and a strict handoff rule.
What changes
Routine questions answered in seconds; the team spends its time on cases that need judgement.
Finance operations
Problem
Supplier invoices arriving as PDFs, keyed into accounting by two people.
System
Extraction pipeline with schema validation, a confidence threshold and a review queue for anything unusual.
What changes
Data entry drops to exceptions only, and every value is traceable to the source page.
Sales
Problem
Inbound leads sitting in a shared inbox, qualified inconsistently.
System
Enrichment, scoring and routing on arrival, plus a drafted first reply queued for a human to send.
What changes
Faster first contact and a CRM that stays accurate without manual updates.
Self-initiated studies, labelled as such. Client case studies are published only with written permission — no invented logos or metrics.
Ledger Lens
Concept — document intelligence
Invoice extraction with per-field confidence and a human review queue for anything below threshold.
Front Desk
Concept — voice agent
After-hours call handling that books appointments and writes a summary back to the CRM.
Atlas Answers
Concept — internal copilot
Retrieval over policies and past tickets, answering with citations and refusing when unsure.
Four stages, each with a clear output — so you always know what happens next.
Thirty minutes on the workflow, the data and what a good outcome looks like. No deck.
The smallest system worth building, with guardrails, evaluation criteria, price and milestones.
Working increments against real examples, with an evaluation set so quality is measured, not assumed.
Monitoring, cost controls, documentation and a team walkthrough. You own the system.
A working system inside your existing tools — support desk, CRM, ERP, website — that uses a model for a specific job, with guardrails, logging and a human fallback. Not a chatbot bolted onto the homepage.
A focused assistant or extraction pipeline usually runs three to six weeks from an approved scope. Larger multi-system work takes longer, and the schedule is agreed in writing before we start.
Model-agnostic. We pick based on quality, latency, cost and data requirements — commercial providers, open-weight models on your own infrastructure, or a mix. The system is built so the model can be swapped.
Not by us, and we configure providers so your content is not used for training where that option exists. For sensitive data we can run open-weight models inside your own cloud account.
Guardrails, schema validation, citations and confidence thresholds decide what the system may answer alone and what must go to a person. Every deployment includes an evaluation set and a review path.
That is the point. We integrate through APIs, webhooks and database access — including WordPress, WooCommerce, HubSpot, Zendesk, Xero and custom internal tools.
You do. Code, prompts, evaluation data and infrastructure live in your accounts and transfer to you on final payment.
Fixed-scope pricing after a scoping call, plus the provider usage costs which are usually small at pilot scale. You get both numbers in the written scope, with a cost ceiling.
A 30-minute call, an honest view of what AI can and cannot do for it, and a written scope if it makes sense.
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