Futurelab builds bespoke AI tools: retrieval and knowledge systems, voice and chat assistants, document processors, and internal operating systems. The engineer who runs discovery writes the code, which keeps the build anchored to the real workflow.
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.