Build the product customers return to. We work with software teams to ship useful AI features, strengthen their platforms and remove the operational work around them, without losing control of customer data, access or cost.
Why software companies are different
Every new capability joins a live product: customer accounts, subscriptions, integrations, support commitments and releases already in motion. AI adds questions about output quality, data access, vendor reliability and unit cost.
We build around the repeated job your customer needs to finish. That can mean a new product, a specific AI capability or a stronger foundation for software you already run. The useful result matters more than the number of models or agents involved.
What we build
For early-stage product teams, growing B2B platforms and established software businesses. These are solution examples, not claims about past deployments; scope and acceptance measures are agreed with your team.
Turn a repeated customer task into a grounded assistant or bounded action: draft a report, extract a document, reconcile records or prepare an update. Keep the result editable and the supporting source visible.
What to verify: Measure task completion, correction rate and cost per completed workflow, not just chat volume.
Connect account setup, invitations, imports and integrations so customers reach their first useful result. Give support teams account-aware context, approved reply drafts and a clear escalation path.
What to verify: Track activation and time to first value without treating a model-generated answer as a resolved issue.
Build organisation memberships, permissions, SSO, audit records and appropriate deployment options around actual buyer requirements. Test account boundaries across files, search, jobs and exports.
What to verify: Verify cross-account access and deprovisioning, not only a successful sign-in.
Improve slow workflows, fragile integrations and difficult releases in an existing product, or build a focused new platform. Use bounded migrations and versioned APIs rather than assuming a complete rewrite.
What to verify: Check existing customer journeys, migration recovery and compatibility before rollout.
AI in the product
Use AI where language, documents or ambiguous inputs make a task difficult. Keep deterministic rules for money, access and contractual limits. Choose models, retrieval or agents only after the workflow and its failure modes are understood.
Search customer documents and records with source access enforced before retrieval. Show citations, respect deletions and avoid mixing one account's context with another's.
Extract information into validated fields, identify exceptions and send uncertain results for review. Keep the original material and corrections available for reconciliation.
Let an assistant prepare or execute specific low-risk actions through typed tools. Server-side permissions, spend limits and approvals govern the work, not the model's instructions alone.
Draft answers from approved product knowledge and account context, with handoff for uncertain, sensitive or unresolved requests. Measure resolution with customer and support evidence.
Use AI to help investigate defects, draft tests and propose changes. Engineers review the code, verify behaviour and own production releases; generated code is not release evidence.
Summarise workflow friction, support themes and adoption patterns from permitted data. Separate observed behaviour from predictions and avoid promising retention or revenue uplift.
One customer request
A customer request stays inside its account boundary. The platform checks access and entitlements, runs the approved workflow, then records the outcome. A model can help do the work; it does not decide who is allowed to do it.
Platform foundations
The account model, subscription policy, integration contracts and operating economics determine whether a capability can become a reliable service. We make those decisions explicit rather than hiding them behind a polished first screen.
People, organisations and subscriptions have separate lifecycles. Invitations, ownership transfers, trial expiry and failed payments need explicit rules.
Verify plan capabilities and usage allowances on the server. Signed billing events, duplicate handling and provider reconciliation keep payment state distinct from product access.
Account-scoped credentials, bounded retries, idempotent writes and reconciliation make failed or uncertain external actions visible and recoverable.
Measure model, storage and integration costs against a completed customer workflow. Set agreed budgets, rate limits and fallbacks before opening broad access.
Security, privacy & reliability
Your customer contracts, supported markets and data types shape the requirements. We translate agreed privacy and security needs into controls and evidence, with your legal and security owners confirming applicability.
Test reads, writes, attachments, retrieval, caches, background work and support access with distinct accounts. A tenant ID sent by the browser is not proof of authorisation.
Agree what reaches a model provider, retention, training use, data location and subprocessors. Keep secrets and unnecessary personal data out of prompts and logs.
Evaluate representative customer tasks, prompt injection, tool misuse and regressions. Keep model-quality checks separate from permissions, billing and workflow correctness.
Use staged rollouts, compatible migrations, rollback plans and tested recovery paths. Assign incident ownership and document degraded behaviour when vendors fail.
Map requirements to controls and evidence with your legal and security owners. SOC 2 reports, ISO 27001 certification and legal compliance are not automatically delivered by adding audit logs.
Deployment choices
Not every software product needs dedicated infrastructure or a self-hosted model. We compare options against real customer requirements, quality evidence and the team's capacity to operate them.
Choose services and model providers against workload, regional availability, customer agreements and operating cost. Shared infrastructure still needs explicit account isolation.
Deploy into an agreed private cloud or dedicated environment where buyer requirements justify it. Network access, keys, monitoring and support responsibilities are defined together.
Consider open-weight models for suitable tasks and data constraints. Benchmark quality, latency and hosting cost before committing; privacy requirements still apply to the surrounding systems.
How we work
A forward-deployed engineer joins your team remotely, works on the agreed problem and is backed by our wider engineering team. We keep product decisions, trade-offs and operational ownership visible throughout delivery.
Work through customer tasks, support issues, commercial rules and the running architecture. Identify one useful outcome and the dependencies that could block it.
Agree account access, data handling, approvals, usage budgets and acceptance checks. Establish a baseline from representative workflows before building AI into them.
Deliver interface, integration, business rules, tests and operating paths together. Your team reviews working increments and the trade-offs behind them.
Check account isolation, failures, migrations and model behaviour. Release to an agreed cohort with rollback, documentation and a named operational owner.
Yes. We review the running product, code, data and release process before proposing changes. A focused feature or staged modernisation is often more practical than a rewrite.
No. We start with a repeated customer task and compare AI against simpler automation. A feature should improve a measurable workflow, not add a chat window without a useful job.
For explicitly scoped actions with server-enforced access and safeguards. Consequential actions can require confirmation or human approval. The model cannot grant itself permissions or change billing policy.
Carry authorised tenant context through database queries, storage, retrieval, caches, queues and integrations. Verify those paths with separate users and organisations, including exports and administrative access.
We can scope identity, provisioning, audit, dedicated deployment and data residency against your buyers' requirements. Provider capabilities, licences and customer configurations remain dependencies.
Measure cost per completed workflow, set agreed account budgets and limit expensive work. Cache only where access permits, choose models based on evaluated quality and provide fallbacks for limits or outages.
No automatic certification or compliance is implied. We implement agreed controls and provide reviewable evidence. Applicable privacy, sector and AI obligations require your qualified advisers and any separately scoped independent assessment.
We estimate after reviewing the workflow, platform and integrations. A paid two-week first sprint targets one agreed goal; it is not a promise to complete an entire SaaS platform.
Your code belongs to you from day one, subject to third-party licences. We hand over agreed code, access and documentation. Ongoing maintenance, incident coverage and service levels are separately agreed.
Let's talk
Tell us about the product, the workflow that slows people down and the constraints your team works within. We will help define a useful first step and the evidence needed to release it.