Overview
Start from the task, not the technology
Most AI projects that stall do so for the same reason: the tool was chosen before the problem was defined. We work the other way round. We look at where your people spend hours on reading, sorting, matching, drafting or answering the same question, and we automate that specific task.
The result is usually not a standalone AI product. It is a feature inside the system your team already uses — a field that fills itself, a document that gets classified on upload, an answer that arrives without someone searching six folders for it.
We also tell you when AI is the wrong answer. A rules engine, a better query or a fixed form is often cheaper, faster and more reliable, and we would rather say so at the proposal stage than after the invoice.
How we engage
- Discovery
- Task and data audit
- Proof of value
- 2–4 weeks, measured
- Build
- Integrated into your stack
- Evaluation
- Accuracy tested on your data
- Data handling
- Your tenant or on-premises
- Human review
- Built into every workflow
Capabilities
What we build with AI
Document & data processing
Invoices, forms, contracts, resumes and scanned records read, classified and turned into structured records that flow into your existing database.
- Extraction from PDFs, scans and images
- Classification and routing
- Validation with human review queues
Internal knowledge assistants
A retrieval-based assistant over your own policies, SOPs, product documentation and past tickets, answering with citations to the source document.
- Retrieval-augmented generation (RAG)
- Source citation and traceability
- Role-based access to content
Customer-facing assistants
Support and enquiry assistants on your website, app or WhatsApp, with a defined scope and a clean escalation to a human when confidence drops.
- Scoped, guard-railed responses
- Handover to live agents
- Conversation logging and review
AI inside your applications
LLM capability added to the software you already run — summaries, drafting, categorisation, matching and search that understands intent.
- LLM API integration
- Semantic search and vector stores
- Prompt design, testing and versioning
Predictive & analytical models
Forecasting, scoring and anomaly detection on your operational data, where the pattern is in the numbers rather than the text.
- Demand and load forecasting
- Risk and lead scoring
- Anomaly and exception detection
Workflow automation
The unglamorous wins: approvals routed automatically, data moved between systems, reports assembled and sent without anyone opening a spreadsheet.
- Multi-step process automation
- System-to-system data flows
- Scheduled reporting and alerts
Governance
The questions your compliance team will ask
We answer these in the proposal, not after deployment.
| Concern | How we handle it |
|---|---|
| Where does our data go? | Deployment into your own cloud tenant or on-premises where required. Model and hosting choices are agreed with you before the build starts. |
| Is our data used for training? | We use enterprise API tiers and configurations where customer content is not used to train the provider's models, and we document the setting in the handover pack. |
| How accurate is it? | Every deployment is evaluated against a test set drawn from your own records, with the measured accuracy reported before go-live rather than claimed. |
| What if it gets something wrong? | Confidence thresholds, human review queues for low-confidence output, and a full audit log of what the system produced and who approved it. |
| Who can see what? | Retrieval respects the same role and permission model as the underlying systems. A user cannot get an answer from a document they are not allowed to open. |
| What does it cost to run? | Token and infrastructure cost modelled per transaction at design stage, with usage monitoring and alerting after go-live. |
Technology
What we work with
Models & APIs
- Anthropic Claude
- OpenAI
- Azure OpenAI Service
- Open-weight models, self-hosted
Retrieval
- Vector databases
- Hybrid keyword + semantic search
- Document chunking pipelines
Data & ML
- Python, pandas, scikit-learn
- SQL and data warehouses
- Scheduled training pipelines
Delivery
- Integrated into web apps and APIs
- Deployed on your cloud or servers
- Monitoring, logging and evaluation
Model and platform names are the trademarks of their respective owners. Selection is made per project against your data-handling, latency and cost requirements.
Tell us what you need to get running.
Share the scope — a project, a team, or day-to-day support — and we will come back with people, timelines and commercials.