Built AI · Service
AI Project Studio. From idea to production system.
Answer
Our end-to-end studio model. We take ownership of an AI initiative from first whiteboard to live system — discovery, architecture, engineering, deployment and iteration — and stay engaged after launch to make it actually work in your business.
What the studio delivers
AI Project Studio is the right shape when you have a clear ambition but want a single senior team owning execution end-to-end. Instead of stitching together a strategy firm, an offshore build shop and an internal product owner, you get one team that designs the system, ships it into production and stays accountable for the outcome it was built to achieve.
We build five categories of AI system inside the studio model: custom AI products, AI agents, AI automations, AI-native internal tools, and integration layers that wire AI into the systems you already run. Most engagements combine more than one.
Custom AI systems
Bespoke applications where AI is the core engine — pricing, underwriting, matching, ranking, generation.
AI agents
Autonomous workflow operators with tool access, guardrails and human-in-the-loop checkpoints.
AI automations
Deterministic workflows with AI judgement embedded where rules cannot reach.
Internal AI tools
Operational dashboards and back-office apps shaped to how your team actually works.
AI-native products
Customer-facing products designed AI-first, not retrofitted with a chatbot.
Integration layers
The connective tissue between CRMs, ERPs, mailboxes, data warehouses and the AI stack.
How the studio runs
Every engagement runs through the same four phases. They overlap, iterate and feed each other — but they exist for a reason: each removes a class of risk before the next begins.
| Phase | Weeks | Output |
|---|---|---|
| Discovery & architecture | 1–3 | Scope, success metrics, data assessment, model choice and full system design |
| Build | 4–10 | Production engineering — pipelines, agents, integrations, frontends, evaluation suites |
| Deploy | 10–12 | Secure rollout into your stack with monitoring, observability and guardrails |
| Iterate | Ongoing | Continuous tuning against real usage data, with quarterly roadmap reviews |
How we staff engagements
Each studio engagement is staffed with a small senior team: an AI engineering lead, one to two AI engineers, a product designer and an embedded operator who runs the programme alongside your team. No layered hierarchies, no rotation of junior staff, no account-management tax. The people who scope the work are the people who build it.
We work in two-week cycles with weekly leadership check-ins. Every decision — model choice, architecture trade-off, scope change — is written down and decided in the open. Your team learns the system as it gets built, so you own it the day it ships.
Engineering principles
Production from day one
We do not build prototypes that need to be re-written. Code shipped in week 4 is the same code running in week 14.
Evaluation before scale
Every AI component has an evaluation suite before it goes live. Quality is measured, not assumed.
Human-in-the-loop by default
Autonomy is earned, not granted. New systems start with humans in the loop and graduate as confidence is proven.
Model-agnostic architecture
We pick the best model per task and design systems so the model can be swapped without re-architecting.
Owned and portable
Your code, your infrastructure, your data. No proprietary runtime, no vendor lock-in.
Observability built in
Logs, traces, evals and cost monitoring are part of the build, not a retrofit.
Where the studio fits
The studio is the right model when the work is genuinely custom and the stakes are high enough to justify a senior team. For early-stage diagnostics, start with the AI Transformation Audit. For pure customer-facing AI products, the studio runs as a product partnership with quarterly cycles. For one-off automations that fit a known template, lighter engagements are available.
FAQ
How long does a typical engagement run?+
Most studio engagements run 8–16 weeks to first production system, with an optional optimisation retainer afterwards. Larger AI-native products run in quarterly cycles.
Do you work with our engineers?+
Yes. We integrate with internal teams when they exist and run independently when they do not. Either way, we ship code your team can own.
What stack do you use?+
Modern AI stack — frontier and open LLMs, retrieval, vector stores, agent frameworks — plus standard cloud infrastructure. We are model-agnostic and stack-agnostic, and we pick what fits the problem.
Who owns the IP?+
You do. We deliver fully owned codebases on your infrastructure with full documentation. No vendor lock-in.
Do you do fixed-price or time-and-materials?+
Both. Discovery and audits are fixed fee. Build engagements are typically milestone-priced with a defined scope; long-running products run as quarterly retainers.
What if requirements change mid-build?+
They will. We run two-week cycles with an embedded operator, so scope is reviewed continuously and changes are absorbed without restarting the engagement.