2 weeks · fixed fee
AI Production Readiness Review
Know what to build before you fund it. An evaluation baseline on your data, a target architecture, a run-cost model and a go/no-go plan.
AI agents that do real work inside your tools
We build agents and LLM applications that carry out multi-step work across your systems: reading documents, updating records, and asking a person to approve when the stakes are high. Every release is tested against an evaluation set before it reaches users.
What is agentic AI development?
From a demo that impresses to a system people rely on
Agent orchestration
RAG, GraphRAG and knowledge graphs
MCP integrations
Human in the loop
An AI agent plans and carries out a sequence of steps toward a goal, using tools such as your CRM, ticketing system or database. The hard part is not the model. It is reliability: the agent has to work as well on a busy Tuesday as it did in the demo.
We design agents with clear scopes, shared memory, permissions, approval points and full traces of what they did, and we measure them on your evaluation set before and after every change. For a UK technology company, a team of four put such a system into production in 6–8 weeks.
01
Workflow agents
Agents that triage, research, draft, update and route work across your tools, with a person approving the steps that matter.
02
Knowledge assistants (RAG and GraphRAG)
Answers grounded in your documents and data, from hybrid retrieval (vectors plus keywords) with reranking, or a knowledge graph where questions span entities. Citations, access control and a measured faithfulness rate.
03
Tool and system integrations
Connections to your APIs, databases and SaaS tools, including Model Context Protocol (MCP) servers your agents and assistants can share.
04
Agent memory and context
A shared context layer, so agents keep business rules, history and state consistent across tasks.
How we take an agent to production:
Guardrails and permissions
Each agent can only touch the tools and data its job needs.
Human-in-the-loop approvals
Escalation rules for decisions that carry risk or cost.
Tracing and observability
Every step an agent took, with inputs, outputs and cost, visible to your team.
Evaluation in CI
Regression tests on every prompt, model or tool change, so quality does not drift.
Model choice by evidence
Hosted or open-weight models chosen on your data, not on leaderboards.
Cost control
Caching, routing and smaller models where they hold quality, measured per task.
Have an agent in a demo that needs to go live?
Read how a team of four put an agent system into production in 6–8 weeks.
How we engage
Start small, with a fixed scope
6–9 weeks · team of 4
Pilot-to-Production Sprint
One workflow, live in production. Built or rebuilt, connected to your tools, with human approvals where the stakes are high and monitoring against the baseline.
Monthly · from 3 months
Forward-deployed AI engineers
Senior AI and data engineers who join your team, work in your tools and ship on your roadmap.
Part-time · monthly
Fractional AI CTO
Senior leadership for your AI roadmap: model and vendor choices, team design, architecture reviews and board reporting.
What clients say
Teams that kept working with us
From my experience, it is hard to find a team who have both excellent technical and commercial skills. Busy Rebel has never let us down, so we continue to work with them. The most important criteria we have as a team is creating & maintaining high standards; Busy Rebel has this in spades.
I've had the pleasure of working with a few software houses in the past, but none of them compares to the team at Busy Rebel. Their sense of mission and purpose is unparalleled, and their in-house skills are excellent. I always know that I can rely on them to get the job done, and done well, even if we are working remotely.
Thanks to the contributions of Busy Rebel, our organization as a massive worldwide distributor has passed all phases of digital transformation, gained a competitive advantage, and somewhere has revolutionized our industry. Busy Rebel is extremely productive digital partner, and internal stakeholders are particularly impressed with the vendor's dedication to success and attention to detail.
Tell us what you’re trying to ship
Agentic AI development: FAQ
What is agentic AI?
Agentic AI describes systems that plan and carry out multi-step tasks toward a goal, using tools and data, instead of answering a single prompt. In a business that means agents that read, decide and act inside your existing systems, with people approving the steps that matter.
How long does it take to build an AI agent?
Our recent agent projects reached production in 6–9 weeks with a team of four. The first two weeks set the evaluation baseline; the rest is the production build, integrations and rollout.
How do you stop agents from making mistakes?
We limit what each agent can access, add approval points for risky actions, trace every step, and test every change against an evaluation set built from your real cases.
Which models do you use?
Whichever performs best on your evaluation set at an acceptable cost: hosted models from the major labs or open-weight models you run yourself. We often pair a large model for planning with smaller ones for routine steps.
When does a knowledge graph beat vector search for RAG?
When a question needs several hops across entities (which accounts on a given contract raised a given issue), or a view of a whole corpus rather than a few passages. On corpus-wide questions, Microsoft Research reported more comprehensive answers from graph-based retrieval than from vector RAG (Edge et al., 2024). For direct lookups, hybrid vector and keyword search is cheaper and usually as accurate. We decide on your evaluation set, comparing accuracy and cost per query. Our AI knowledge graph shows how these methods connect.
Who owns the system after launch?
You do. Code, prompts, evaluation sets and documentation are yours, and we hand over runbooks so your team can operate and change it.
