AI & Workflow Automation
AI that takes work off your team's desk, with accuracy you can check.
Document processing, support triage, search over your own documents, data extraction and workflow automation across systems. Each feature ships with a test set drawn from your data, guardrails and a human review queue, so you know what it gets right and what it gets wrong before it goes live.
What you end up with
- Take hours of repetitive processing out of finance, operations and support each week
- Answer customer and staff questions from your own documents, with the source cited
- Turn emails, PDFs and scanned forms into structured records without anyone re-keying them
- Know the accuracy of each AI feature as a percentage, before launch and every week after
Most projects start with a fixed-price discovery, so you have a scoped plan and an estimate in hand before you commit to a build.
Overview
Where language models are useful in a back office
Language models are now good enough to do work that used to need a person reading something and re-typing it somewhere else, such as pulling the fields off invoices and delivery notes, sorting support tickets and drafting the reply, summarising a case file or answering a question from a long policy document. Put that together with ordinary workflow automation (rules, queues, integrations, scheduled jobs) and you have covered a large share of the manual effort in most back offices.
The gap between a demo and a system you can rely on is engineering, and most of it is unglamorous. Every AI feature we build has an evaluation set made up of your real documents, so accuracy is a percentage we can show you. Outputs are structured and validated, and a bad answer fails safely. Where an error is expensive, a person reviews the uncertain cases before anything goes out. Cost and latency have hard limits, so neither the bill nor the response time can drift. And if a rules engine or a redesigned form would do the job better than a model, we will say so and build that instead.
What's included
The kinds of work this covers.
- 01
Document processing and data extraction
Invoices, purchase orders, contracts, forms and letters turned into structured, validated records. Each field carries a confidence score, and anything below the threshold lands in a review queue for a person to check.
- 02
Support and operations triage
Classifying, routing and prioritising tickets, emails and web forms, and drafting the reply. Escalation rules and agent review sit in front of the send button so nothing wrong goes out unseen.
- 03
Search and assistants over internal knowledge
Retrieval-augmented question answering across policies, manuals, past tickets and case history. It respects the document permissions people already have and cites the source for every answer, so staff can check it.
- 04
Workflow automation and orchestration
Multi-step processes that cross several systems (approvals, reconciliations, onboarding, month-end reporting) automated with rules, queues and integrations, plus RPA-style steps for the one system that has no API.
- 05
Evaluation, guardrails and monitoring
Test sets built from your real data, regression evaluations that run in CI on every change, prompt and model versioning, output validation, PII handling, and production monitoring of accuracy, cost and latency.
- 06
AI opportunity assessment
A short engagement to find where AI or automation would pay back fastest in your operation, what data you have to work with, and which ideas are not worth pursuing. Each option gets an estimate, including the ones we advise against.
Is it right for you?
A good fit if
- A team spends much of its week reading documents and re-keying what it finds into another system
- Support or operations volume is growing faster than you can hire
- Staff cannot find answers that are somewhere in the policies, the manuals or a past case
- You ran an AI pilot that looked good in the demo and never reached production
- You want automation you can audit, explain to a regulator and switch off if you have to
How we approach it
The steps between a first call and go-live.
Find the right problem
Two to three weeks mapping the workflow, counting the hours of manual effort, checking what data exists and what the privacy constraints are, and agreeing what accuracy and cost per item would make the automation worth doing.
Prove it on your data
A working prototype scored against a labelled sample of your own documents or tickets. You see the accuracy figure and the cost per item before deciding whether to build.
Build with review and safeguards
Production integration with structured outputs, validation, confidence thresholds, a human review queue, an audit trail, and the evaluations running in CI on every change.
Launch and tune
A staged rollout with monitoring of accuracy, cost, latency and how often reviewers override the model. Then a cycle of tuning, widening coverage and retiring manual steps as the numbers earn it.
Deliverables
What you'll have at the end.
- Opportunity assessment with shortlisted use cases, a data review and estimates
- Prototype scored on your data, with accuracy and cost-per-item figures
- Production AI or automation feature connected to your systems
- Evaluation suite, labelled test sets and regression checks in CI
- Review queue for the uncertain cases, an audit trail and monitoring dashboards
- Data handling and privacy documentation for your DPO
Typical stack
The tools we tend to use for this.
- Python
- TypeScript
- OpenAI / Anthropic APIs
- LangChain / LlamaIndex
- pgvector
- PostgreSQL
- Node.js
- AWS
- Grafana / OpenTelemetry
- Elasticsearch
Questions
Questions about this service.
How accurate will it be?
We do not guess. The prototype stage scores the model on your own data and reports accuracy as a percentage, with a breakdown by confidence, so you can decide which cases go straight through and which go to a person. Accuracy is then monitored in production and re-checked every time anything changes.
What about our data and privacy?
We design for it from the start. Providers and regions are chosen to fit your obligations, the API terms rule out training on your data, personal information is redacted where it can be, and for sensitive material we run the model privately. The data flow and retention are written up for your DPO.
How much does an AI project cost and how long does it take?
An assessment and a scored prototype typically run £15k to £40k over four to eight weeks. A production feature usually follows in another two to four months. Ongoing model costs are estimated during the prototype, so you know the running cost before you commit to the build.
When would you advise against using AI?
Often. If the process follows fixed rules, a rules engine is cheaper and easier to explain. Poor data is a reason to fix the data first. And if a mistake is expensive and nobody is available to review the output, a good demo is not enough to go live on. In those cases the assessment recommends against it and we stop there.
Can we run this on our own infrastructure?
Yes, where the case justifies it. Open-weight models run on your own cloud account or on hardware in your building. We help you weigh the accuracy, cost and operating effort against the hosted APIs, and the answer is different for each case.
Related services
Industries
- Professional Services & B2BClient portals, engagement tools and internal platforms for firms that sell expertise.
- Manufacturing & IndustrialShop-floor, quality and planning software connected to your machines and ERP.
- Fintech & Financial ServicesLending, payments and wealth platforms for firms the FCA regulates.