Futurelab designs and builds AI agents that research, retrieve, draft and coordinate inside the limits your organisation sets. Each agent has one job, trusted context, scoped tools, guardrails and human review, and is tested on real work before launch.
AI agent development
We design and build AI agents that research, reason, retrieve, draft and coordinate inside the boundaries your organisation needs.
Futurelab Studios: OpenAI Select Partner · Claude Partner Network
Trusted by teams at PepsiCo · ITC · SBI · Syngenta
A useful agent is not a chatbot with a longer prompt. It is a small system, and each part is designed on purpose.
A narrow, valuable workflow with an owner and a measure of success, such as answering policy questions or briefing a sales call.
Grounded in approved documents, data sources and business rules, so answers can be traced back to something real.
The systems it can read from or write to, such as search, calendars, CRMs and documents, each with scoped access.
Permissions, safety boundaries and rules for what it must never do, agreed with your security and risk teams.
Human review points, evaluation against real work and monitoring after launch, so quality is checked, not assumed.
These are products Futurelab built, each an agent or assistant with one job. Status is shown as it stands today.
A knowledge agent: answers questions from your own documents, so teams get accurate replies instantly.
A retrieval agent: keeps what your organisation knows searchable, organised and up to date.
A research agent: gathers, analyses and summarises market and competitor research in minutes.
A meeting-to-action agent: joins meetings, transcribes them and delivers clear action items.
A client agent: tracks interactions, flags risks and suggests next steps for each relationship.
A voice assistant: speech to text and voice commands for workflows across the organisation.
The best business agent is not a general-purpose chatbot. It has trusted context, a defined job and accountable human oversight.
Agents designed around a narrow, valuable business workflow, with a named owner and a measure of success.
Grounded in approved knowledge, data sources and business rules, with answers that can be traced to a source.
Appropriate permissions, review points, evaluation and monitoring, from the first prototype to long after launch.
Useful, not theatrical Every agent is connected to a specific workflow, a clear owner and measurable outcomes, so it earns its place in the day.
These are the shapes we are asked for most often. Each is designed around one workflow, then tested on representative work before it goes live.
Answers internal questions using approved policies and documents.
Monitors a market, summarises what changed and prepares usable briefs.
Prepares accounts, synthesises signals and suggests next actions.
Coordinates repetitive information tasks across a defined process.
Four steps, each with a clear output, so you always know what is being tested and what comes next.
Choose an agent opportunity with real frequency, friction and business value, and name its owner.
You get: A scoped use case and success measure
Define context, actions, safeguards and a usable interface, then build a working prototype.
You get: A prototype on your own data
Evaluate output quality with the people who do the work, against representative examples.
You get: An evaluation you can explain
Launch with the right controls, monitor how it performs and keep improving what works.
You get: A live agent with monitoring and owners
Embedded deployment with the full advisory team—custom AI tools that shortened planning cycles and improved coordination.
Technology leadership and product delivery—from AI Sachiv at state scale to meeting intelligence and learning platforms adopted as a digital public good.
An agent has a defined job and can use approved tools or data to complete steps toward an outcome. A chatbot mostly answers within a conversation. An agent might look up a policy, draft a reply and route a case to the right person.
Potentially, with scoped permissions and the security approach agreed for the project. We start with read-only access where possible, limit each agent to the systems its job needs, and keep a record of what it did.
We define success cases with the people who do the work, test the agent against representative examples and add review controls where needed. After launch we monitor performance, so quality is checked and not assumed.
Yes. A narrow pilot on one workflow is normally the best path to useful learning. It limits risk, shows value quickly and tells you what to build next.
The most common are knowledge agents that answer from your documents, research agents that monitor and summarise, sales agents that prepare accounts, and operations agents that coordinate routine information tasks. Each is designed around one workflow.
It depends on the workflow, the data and the integrations involved. A focused pilot can usually be scoped, prototyped and tested in weeks, not years. We agree the plan and milestones with you before work begins.
Your team does. We document how the agent works, how it is evaluated and how it is run, and we enable your people to operate and extend it, so you are not dependent on us for day-to-day use.
Tell us about the workflow and who owns it. We will come back with a practical view of what an agent could do, and where to start small.