Futurelab designs and builds custom AI software around how an organisation works, from knowledge systems and planning copilots to citizen-facing assistants. Recent work includes AI Sachiv, adopted across Bihar, Jharkhand and Haryana, and a suite of planning tools for a financial advisory firm.
Custom AI solutions
From knowledge copilots to operational tools, we build focused AI products that fit your data, your users and your real-world constraints.
Futurelab Studios: OpenAI Select Partner · Claude Partner Network
Built with teams at Piramal Foundation · International Money Matters · PepsiCo · ITC
We work from a valuable business problem toward a deployable product, with the right interface, context and guardrails at every stage.
Define users, outcomes, workflows, data and constraints before a line of code. The brief names who uses it and what changes.
A working prototype on your own data in short, feedback-led cycles, so users react to the real thing early.
Models, retrieval, integrations and interface chosen for the job, with security and data boundaries agreed up front.
Training, measurement and iteration after release, so the product is adopted and improved, not just delivered.
Sometimes the fastest route is a product we have already built, configured for your organisation instead of built from scratch.
A chatbot trained on your own documents, so teams get accurate answers instantly.
Client-ready proposals with sections and sources, exported to Word, PDF or PowerPoint.
Social listening that shows sentiment, themes and how influence moves across your audience.
A few of the AI products we have designed and built with clients, each anchored to one job inside their organisation.
Off-the-shelf AI often falls short of how an organisation really works. A focused product closes that gap, if it is designed around the work.
Start with the decision or workflow that needs to improve, and the people who own it, before choosing any technology.
Choose models, retrieval, integrations and interfaces to suit the job, and use standard software where AI is not needed.
Build with users and operating reality in view from the first prototype, so the product fits the day it launches.
Built to be used Useful interfaces, working systems and support for adoption. We measure success by whether people use the product, not by whether it was delivered.
Most requests fit one of these shapes. Each is designed around your data, your users and the boundaries your organisation sets.
Make organisational knowledge easier to find and use.
Bring inputs, assumptions and decision logic into one useful workspace.
Turn operational data into prioritised answers and actions.
Deliver helpful, controlled guidance at the point of need.
Short, feedback-led cycles, with a clear output at each stage so you always know what is ready.
Define users, outcomes, workflows, data and constraints, and agree how success will be measured.
You get: A product brief and success measure
Prototype and develop the product in short, feedback-led cycles with the people who will use it.
You get: A working product on your data
Support rollout, measurement and further iteration after release, with ownership handed to your team.
You get: A live product, documented and owned
We now view AI not as a threat to financial advisory, but a lever for deeper trust and insight. Futurelab helped us get there.
Lovaii Navlakhi, Founder & CEO
Development timelines vary based on complexity, scope, and requirements. Simple tools like chatbots can take 4-8 weeks, while complex systems with multiple integrations may take 3-6 months. We provide detailed timelines after understanding your requirements.
The data requirements depend on your use case. For knowledge management systems, we need access to your documents and knowledge base. For chatbots and voice assistants, we may need training data, conversation logs, or domain-specific content. We discuss your specific data needs during the discovery phase.
Yes. Integration with existing systems is a core part of our custom development process. We work with APIs, databases, CRMs, ERPs, and other enterprise systems to ensure seamless integration.
Security is paramount in all our developments. We follow industry best practices for data encryption, access controls, and compliance. We can build on-premise solutions or use secure cloud infrastructure. We also help ensure compliance with relevant regulations like GDPR, data protection laws, and industry-specific requirements.
We provide comprehensive ongoing support including monitoring, maintenance, updates, and optimization. We offer different support tiers based on your needs, from basic maintenance to dedicated support with regular improvements and feature additions.
Custom tools are built by the same forward deployed engineers who run the discovery. The engineer who mapped your workflow writes the code, which keeps the build anchored to how the work actually happens instead of to a written specification that ages badly.
Agent cost is driven by how many tools the agent must use, how reliable the output has to be, and what happens when it is wrong. A read-only agent answering questions over internal documents is bounded work. An agent that takes actions in live systems needs evaluation, guardrails and rollback, and costs accordingly.
The useful question is not which agent but which task. Agents work well where a process is repetitive, rule-bound and currently consumes people time: document handling, internal question answering, routing and triage, structured data entry. They work badly where judgement is the point. Choosing the task correctly matters more than choosing the underlying model.
Start from the workflow, not the framework. Map what a person does today, identify the steps that are mechanical, and build the agent against those with a human check on anything consequential. Build the evaluation set at the same time as the agent, so you can tell whether a change improved things or just changed them.
Often yes, because startups have fewer legacy systems to integrate and can change a process by deciding to. The constraint is usually attention rather than budget: an agent still needs an internal owner. Where no one has time to own it, a smaller automation that needs no supervision is usually the better first step.
Usually yes, through existing APIs or the automation layer your stack already has. Integration difficulty is the main driver of both cost and timeline, so it is confirmed during scoping rather than assumed. Where a system has no API, the honest answer is sometimes that a different workflow is the better first candidate.
Yes. Agent and chatbot work is delivered remote-first for clients across India, including Bengaluru. What decides the project is the task rather than the location: agents work well on repetitive, rule-bound processes and poorly where judgement is the point.
Ask to see an agent running against real data, not a scripted demo. Ask what its evaluation set looks like and what happens when it answers wrongly. Teams that cannot answer the second question have usually built a prototype rather than a system.
No. We use the right combination of AI, data and standard software for the business problem. Where a rule or a simple tool does the job better, we use that.
Yes. We can augment your team, work in parallel or take ownership of a defined product, and we hand over documentation so your team can run and extend it.
Recent work includes AI Sachiv, a citizen-facing assistant adopted across Bihar, Jharkhand and Haryana, plus meeting capture and a learning platform for Piramal Foundation, and a financial calculator, planning tool and coordination chatbot for International Money Matters.
Describe the workflow, the users and the data involved. We will come back with a practical view of what to build first.