Your teams are still doing work that AI should be doing.
87% of AI projects never reach production. Not because AI doesn’t work – because most teams don’t know how to ship it. Agent Restive does.
We turn AI theory and ambition into capability, behaviour and results.
In a world being rewritten by AI, the winners in the next decade won’t be the ones with the best AI strategy decks, but the ones with AI embedded in how work actually gets done, every day, by real teams, in real systems.
We design, build and operationalise production-grade AI that replaces friction, augments people, and creates durable advantage. From agents and automation to decision intelligence and applied machine learning, we focus on AI that ships, scales and compounds.
The Agent Restive difference
AI agents run our discovery. Not PowerPoint
From the first session, an AI agent is in the room - eliciting requirements, asking the right questions, capturing decisions in real time. We don't present. We build.
Working software by end of week 1
Not a wireframe. Not a demo video. A deployed agent your team can test - an AI that answers calls, processes documents, or serves customers. Real software. Real feedback.
We ship the operating model alongside the code
Backlog. Repo. Deployment pipeline. QA process. Observability. The full operating framework - not just the agent. So your team can run it, iterate on it, and keep building.
How we work: Three ways to engage
01 — Rapid PoC & Product Launch
Idea to working software by end of week 1.
Most AI projects die waiting for sign-off on the strategy deck. We skip that part.
Week 1 — Discovery Sprint + Working Prototype
An AI agent runs the discovery session. It asks questions, processes your documents, elicits requirements, and builds the specification in real time – while our BA/PM and AI Orchestrator work alongside your team. By end of week 1:
- Requirements documented and agreed
- Solution architecture designed
- Backlog and roadmap loaded into your project tooling
- Infrastructure scaffolded: environments, deployment pipeline, QA, PR process all set up
- Roadmap ready to run
- We bring the team. You bring the problem.
By end of week 1 — Working Prototype
We build it. A functioning agent, live in a real environment. Give your stakeholders a link, a phone number, or a dashboard — something they can actually use and respond to. Not “what you’ll get.” What you have.
Weeks 2–8 — Build to MVP
Structured sprint cycles. Continuous deployment. Agents write the code. Humans review, test, and ship. We compress months of development into weeks — then hand over a production-grade product and an engineering team that knows how to run it.
02 — AI Transformation & Operating Model
For organisations embedding AI across teams, not just one use case.
This isn’t a strategy engagement. It’s a delivery programme that changes how your organisation operates.
We embed a small, high-leverage team directly into your business:
- A BA/PM/Service Designer who works alongside your people – mapping real processes, not assumed ones. If you’re replacing an ERP, they’re inside the business understanding every workflow, every handoff, every decision point.
- An AI Orchestrator who translates that into agent architecture – then builds it.
- A specialist agent team that ships, iterates, and scales.
- The output isn’t a roadmap. It’s running AI – embedded in your tools, trained on your data, operated by your team.
Where this works: Process transformation. ERP replacement. Back-office automation. Compliance and reporting. Customer operations. Anywhere people are doing work that compounds over time and never really gets faster.
03 — Labs & Joint Ventures
Co-build. Co-own. Co-commercialise.
Sometimes the problem we’re solving for one client is actually a product. When that’s the case, we don’t just build and hand it over.
The model: the client co-funds the build. We bring the infrastructure, the AI Orchestrator, and the delivery team. If the solution is scalable, we structure a commercial arrangement – revenue share or equity – to take it to market together.
An example in progress: an AI agent built for a veterinary hospital. If it works at scale — and the early signals say it will – it becomes a product for every hospital in the network. Not a one-off engagement. A business.
This is for founders, heads of product, and enterprise teams who want to move fast and build something that compounds.
Agent infrastructure: MCP servers, multi-agent orchestration, skills files, context engineering, custom connectors
Models: Google Vertex AI, AWS Bedrock, Anthropic Claude, OpenAI – we pick the right model for the job
Tooling: Your existing stack – we work with whatever project management, communication, and engineering tools your team already uses
Observability: evals, tracing, human-in-the-loop controls, audit trails
Cloud: Google Cloud, AWS, Azure
Our focus areas
Agent systems and orchestration
We design the control plane for AI. Multi-agent systems that give AI structured access to your tools, data, and actions – so agents don’t just respond to prompts, they run processes.
In practice:
- Map your toolset and data boundaries
- Design agent roles, communication patterns, and handoffs
- Connect via MCP servers, APIs, CLIs, custom connectors
- Instrument with evals, observability, and rollback from day one
Customer agents and AI experiences
Agents your customers interact with directly. They guide, recommend, resolve, retain, and convert – grounded in your data, your policies, your brand. Not a generic chatbot. Yours.
In practice:
- Discovery sprint: map the highest-value customer touchpoints
- Design for trust: persona, tone, guardrails, escalation paths
- Integrate with your commerce stack, CRM, and knowledge bases
- Deploy with monitoring, feedback loops, and continuous improvement built in
Workflow automation and internal agents
Internal agents that execute work – not just assist it. The goal: take what takes your team hours and make it take seconds.
In practice:
- Process discovery: find the highest-ROI automation opportunities
- Agents run inside the tools your teams already use – project management, ticketing, communication, and operations platforms
- Every workflow is auditable, traceable, compliance-ready
- Measurement from day one: time saved, throughput, error rates
AI-first product development
Building new products where AI is the product – not a feature added later.
In practice:
- AI-facilitated product discovery: what to build, for whom, with what agent capabilities
- Agent-native architecture: model selection, fine-tuning, eval frameworks, multi-tenant design
- Cost-to-serve optimisation built in from the start – not as an afterthought
- Continuous learning loops so the product gets better in production
AI transformation and operating model
Making AI stick across an organisation – not one team’s experiment, but a new way of working.
In practice:
- Governance and risk: what’s the exposure, what’s the readiness, what do we sequence first?
- Use case prioritisation: highest-ROI first, compounding value over time
- Embedded delivery team: BA/PM + AI Orchestrator + agent specialists inside your org
- Playbooks and measurement so your team can operate it – or hand back to us for delivery
AI in production
Our clients
From idea to working AI - in one week.
Book an AI Discovery Call. We’ll identify your highest-value AI opportunity, walk you through the approach, and tell you exactly what you’d have in your hands by the end of week 1.