Services
AI development services with measurable, auditable outcomes
Each AI capability we build has a narrow purpose, an accuracy target agreed in advance, human review where consequences matter, and a known monthly running cost.
Start with the task and the measure, not the model
Most disappointing AI initiatives began with “we should use AI” rather than “this task consumes twenty hours of our team’s week.” We begin with the second. Which task is repetitive, text-heavy or low in judgement? How will we know it is working? What happens when it is wrong?
Answering those questions first means the system has an accuracy target, a fallback when confidence is low, and a log of every decision your team can audit. It also means some ideas are retired early, which is far cheaper than discovering their limits in production.
What we build
AI capabilities we deliver
Document extraction
Invoices, purchase orders, KYC documents, lab reports and shipping papers converted into structured records, with a review queue for uncertain fields.
Search over company knowledge
Answers drawn from your manuals, SOPs, contracts or ticket history, with citations to source passages so staff can verify.
Classification and routing
Incoming emails, tickets and leads sorted by type, urgency or department so they reach the right owner first time.
Drafting assistants
First drafts of quotations, reports, replies and product descriptions in your format, reviewed by a person before release.
Forecasting and anomaly detection
Demand estimates, churn signals and unusual transactions from historical data, presented as flags for review rather than automated decisions.
How we work
How we deliver AI capabilities
Step 1: Define the task and the measure
We agree one task, a sample of real inputs and the acceptance threshold, for example the share of invoice totals extracted correctly.
Step 2: Feasibility test
A short experiment on your own samples to establish how current models perform before any commitment to a full build.
Step 3: Design human oversight
Where people review, approve or correct outputs, and how those corrections feed back into the system.
Step 4: Embed in the workflow
The capability sits inside your existing application, inbox or WhatsApp flow, not in a separate tool people forget to open.
Step 5: Guardrails and cost controls
Data handling rules, input and output logging, usage limits and a dashboard showing monthly running cost.
Step 6: Measure and tune
Results are tracked against the agreed measure in the first weeks, and prompts, retrieval or rules adjusted based on real errors.
Deliverables and tools
Deliverables and technology
What you receive
- Feasibility report with measured accuracy on your own data
- AI capability integrated into your application or workflow
- Review and correction interface for human oversight
- Logging of inputs, outputs and decisions
- Usage and cost dashboard with spending limits
- Documentation of prompts, data sources and known limitations
Models and tooling
- Anthropic Claude, OpenAI and Google Gemini APIs
- Open-weight models where data must remain on your servers
- Vector search with pgvector or Qdrant
- Python for data preparation and evaluation
- OCR for scanned documents
- Evaluation sets built from your own examples
Engagement model and running costs
We usually begin with a two- to three-week feasibility phase at a fixed price. You receive a working prototype on your own data and a measured accuracy figure. If the results do not justify a build, you have learned that at modest cost.
A production capability typically follows in six to twelve weeks. AI also carries a running cost: hosted models are billed by usage. We estimate this during feasibility, select the smallest model that meets the accuracy target, and enforce spending caps.
Often paired with AI chatbots, business automation and our practical guide to AI automation.
Where we draw the line
We do not build AI that makes final decisions about a person’s health, credit, employment or legal position without human sign-off. We will also tell you when a plain rule or a well-designed form would perform more reliably than a model.
FAQ
Questions about AI development
Is our data used to train the AI provider’s models?
On the business API tiers we use, providers state that customer data is not used for training by default. We confirm current terms for your chosen provider, and for sensitive data we can deploy models on infrastructure you control.
How accurate will it be?
That cannot be known until it is tested on your documents, which is why we start with feasibility. You receive a measured figure on real samples, not a vendor claim.
What happens when the AI is wrong?
Every system has a fallback. Low-confidence results go to a person, every output is logged, and corrections are recorded so error patterns can be identified and fixed.
What will it cost to run each month?
It depends on volume and model. During feasibility we measure the cost per document or conversation, project it against your expected volume, and set limits so the bill cannot surprise you.
Have a task you believe AI could absorb?
Send a description and, if possible, a few anonymised samples. We will tell you whether it merits a feasibility test.