AI agents & automation
Agents that plan, call your tools and APIs, and finish workflows end-to-end, with approval steps where money or risk is involved.
- Ops & back-office agents
- Sales and SDR agents
- Research and reporting agents
Labs / AI Products
Practice 01 · AI Products & AgentsAI that does real work, measurably.
We design, build and operate production AI: copilots inside your product, agents that complete multi-step tasks, assistants grounded in your own knowledge, and entirely new AI-native products. Every system ships with evaluations, guardrails and cost controls.
What we build
Six kinds of AI product we ship.
We start from the job to be done and the data you already have, then choose the lightest architecture that reliably does it.
Copilots in your product
Embedded assistants that make your existing SaaS or platform dramatically faster to use.
- Natural-language search & actions
- Drafting, summarising, explaining
- In-app onboarding guides
Knowledge assistants (RAG)
Assistants grounded in your documents, tickets and databases, with citations, access control and freshness.
- Customer support deflection
- Internal policy & SOP bots
- Sales enablement answers
Voice & conversational AI
Phone and chat agents for booking, qualification and support, integrated with your calendar and CRM.
- Inbound call answering
- Appointment booking
- Multilingual support
Document & data intelligence
Extraction, classification and analysis over contracts, invoices, forms and unstructured data.
- Structured extraction pipelines
- Contract & compliance review
- AI analytics over your warehouse
AI-native SaaS
New products where AI is the core value, from zero-to-one MVP through launch and scale.
- Product strategy & UX
- Multi-tenant platform
- Usage-based billing
Engineering standard
What separates a demo from a product.
Anyone can wire a prompt to a chat box. These are the parts we treat as non-negotiable on every AI build.
01
Evaluation suites
A golden set of real inputs with expected outcomes, scored automatically on every change and every model upgrade.
02
Guardrails & safety
PII redaction, prompt-injection defences, permission-aware retrieval and human approval for high-stakes actions.
03
Observability
Full tracing of every model call and tool use, with dashboards for quality, latency and cost per task.
04
Cost engineering
Model routing, caching and batching so unit economics work at 100× your launch volume.
05
Model portability
An abstraction layer so you can move between providers as prices and capabilities change.
06
Data governance
Clear data-flow documentation, retention rules and vendor terms reviewed for your compliance needs.
Use cases by function
Where AI pays back fastest.
A starting map. Discovery tells us which of these is worth building first for you.
Customer support
Deflect repetitive tickets, draft replies for agents, and auto-tag and route the rest.
Sales & marketing
Lead research, personalised outreach drafts, call summaries pushed into the CRM.
Operations
Order exceptions, invoice processing, supplier comms and reporting on autopilot.
Product
Copilots, smart search and personalisation that raise activation and retention.
Questions
AI, answered plainly.
Will our data be used to train someone else's model?
No. We use enterprise API terms that exclude training on your data, and can deploy on private cloud or open-weight models when required.
How do you stop the AI making things up?
Grounding in your own sources with citations, constrained outputs, evaluation suites that specifically test for unsupported claims, and fallbacks to a human when confidence is low.
Which model do you use?
Whichever wins on your eval set for quality, latency and cost. We frequently use different models for different steps of the same product.
What does an AI prototype cost?
Most two-to-three-week prototype sprints sit in our Prototype engagement band. See the engagements page for current ranges.