We build AI applications that run in production: retrieval systems over your own content, internal tools that remove manual work, and agents that act inside your business systems.
A convincing AI demo takes a weekend. A production AI application takes evaluation harnesses, retrieval that returns the right documents, permission models, cost control, latency budgets, observability and a plan for what happens when the model is confidently wrong. Most projects underestimate the distance between those two states, ship the demo, and then spend months discovering the missing pieces in front of users.
We build AI applications and MVPs the way we would build any production system — typed, tested, observable and deployable — with the model treated as one component among several rather than as the whole product.
End-to-end product development where the AI capability is embedded in a real application with authentication, state, billing and an interface people can use.
A narrow, genuinely usable first version that tests the core hypothesis with real users, built on foundations that will not need throwing away if it works.
Agents with defined tool access, bounded autonomy and full logging, so every action is attributable and reversible.
Retrieval-augmented systems where the hard work goes into chunking, indexing, re-ranking and citation rather than into prompt wording alone.
Whether the product is sold externally or used internally, the engineering standards are the same; only the surface area differs.
The application connects to the systems your business already runs on, and it deploys with the automation and monitoring a production service needs.
Typical builds are a customer-facing assistant grounded in your documentation, an internal tool that drafts or classifies at volume, or an agent that reads and writes to your CRM and ticketing systems.
Applications ship with typed APIs, row-level security on customer data, structured logging of model calls, cost tracking per feature, and a rollback path. You own the repository and the infrastructure accounts.
Typical situations where teams bring us in for this work.
Retrieval over approved internal documentation with citations and permissions that mirror existing access controls.
An AI capability inside an existing product, built to the same reliability and latency standards as the rest of the app.
Structured data pulled from contracts, invoices or forms with validation and human review on low-confidence results.
Tools that draft, check and execute repetitive specialist work under human approval.
Exposing internal systems to AI clients over the Model Context Protocol with scoped, audited access.
The same delivery method every engagement follows — see our full process for more detail.
Users, jobs to be done, constraints, data sources and the definition of success. We write down what the system must not do as well as what it must.
Data model, service boundaries, model and retrieval strategy, security posture, cost and latency budgets — decided before code.
The riskiest part built first on real data, evaluated against a labelled set so viability is proven before full build.
Retrieval, prompting, tool access, structured outputs, guardrails and fallbacks wired into the application.
Iterative delivery in short cycles with working software you can use at the end of each one.
Unit, integration and evaluation-set testing, load testing where it matters, and a security review before launch.
CI/CD, environment promotion, monitoring, alerting and a documented rollback path.
Post-launch work on quality, latency and token cost, driven by production telemetry rather than assumption.
We are vendor-neutral. These are the platforms we work with most often on this service.
Consultants spent significant time locating precedent documents and prior project material spread across several repositories.
Read the case study E-commerceAd platform numbers, GA4 and the finance report disagreed by a wide margin each month, so channel budget decisions were being argued rather than decided.
Read the case studyYes. Code is delivered into your repositories and infrastructure accounts, with documentation and a handover session.
Yes. A large share of our work is adding AI capability to an application that already has users, which usually means working inside your existing codebase and release process.
We track token spend per feature, cache aggressively, route simple tasks to smaller models, and set hard budget ceilings with alerting.
Test integration endpoints in the browser without CORS pain.
Website audit, AI readiness, DNS, SSL, API and MCP testing tools.
Challenge, architecture, stack and measured results from real engagements.
Our discovery-to-optimization delivery method, step by step.
Working prototypes and MVPs in weeks
ServiceAI applied to segmentation, content operations and decisioning
ServiceStack strategy, vendor selection and architecture
SolutionFrom idea to a usable product with real users
SolutionAgents that act inside your marketing systems
Bring us the problem and the constraints. We will come back with an architecture, a scope we can defend and a realistic timeline.
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