Futurelab builds enterprise AI chatbots for customers, employees, field teams and citizens. Each chatbot answers from approved sources with citations, respects who is asking, takes actions in connected systems and hands over to a person. Shipped examples include AI Sachiv for Piramal Foundation, adopted across Bihar, Jharkhand and Haryana.
Enterprise AI chatbots
We build AI chatbots for enterprises: grounded in your own documents and data, aware of who is asking, able to act in your systems and quick to hand over to a person. For customers, employees and field teams.
Futurelab Studios: OpenAI Select Partner · Claude Partner Network
Trusted by teams at PepsiCo · ITC · SBI · Syngenta
Most chatbots are a website widget with a long prompt. An enterprise chatbot is built differently in five places. Switch between them and see what changes.
Grounded in approved policies, product data and documents, with the source shown, instead of whatever the model remembers from the internet.
Signs people in and respects permissions, so a customer, an employee and a manager each see only what they are allowed to.
Checks an order, raises a ticket, books a slot or updates a record in your systems, rather than only describing how to.
Recognises when it is out of its depth and passes the conversation to a person, with the history attached.
Every conversation is logged and reviewed, with dashboards for resolution, handovers and the questions it could not answer.
Futurelab builds and runs its own AI assistants. Client chatbots start from what we learned shipping these; status is shown as it stands today.
A chatbot trained on your own documents, so your team always gets accurate answers instantly.
Keeps what your organisation knows searchable and up to date: the knowledge layer under a good chatbot.
An assistant for client teams: tracks interactions, flags risks and suggests next steps.
Two chatbots from our published case studies: one serving citizens across three states, one inside a financial advisory firm.
An enterprise chatbot is worth building when it resolves real questions end to end, takes load off your teams and stays inside your rules.
People get a correct answer or a completed request at any hour, instead of a link to an FAQ page or a queue.
Support, HR, IT and operations stop answering the same questions every day and focus on the cases that need them.
Permissions, approved sources, review and logging are part of the build, agreed with your security and compliance teams.
Grounded, governed, measured If a chatbot cannot show where an answer came from, who was allowed to see it and how often it gets things right, it is not ready for your customers.
Each one is scoped to a clear audience and job, connected to the systems it needs and tested on real questions before launch.
Answers and resolves customer questions on your website, app or messaging channels.
One place for staff to ask HR, IT, finance and policy questions.
Answers for sales, field and partner teams on the move.
Helps citizens and beneficiaries find services and information.
Four steps, each with a clear output, so you can try the chatbot on real questions before it meets your customers.
Collect the real questions people ask, decide which the chatbot should answer, act on or hand over, and agree how success is measured.
You get: A question set and success measures
Bring in approved documents and data, connect the systems it needs and set who may see what.
You get: A working chatbot on your content
Run it against the question set and with real users, fix wrong or weak answers and tighten the guardrails.
You get: An evaluation you can show your risk team
Go live on your channels with monitoring, handover and a regular review of unanswered questions.
You get: A live chatbot with owners and reporting
An enterprise chatbot answers from your approved documents and data with sources, knows who is asking and what they may see, takes actions in your systems, hands over to a person when needed, and is logged and measured. A basic widget usually does none of these.
Yes. That is the core of the build. We connect approved sources such as policies, product data, manuals and order systems, and the chatbot answers from them, citing where each answer came from.
Your website or app, Microsoft Teams or Slack for employees, and WhatsApp for customers, partners and field teams. The same knowledge and rules sit behind every channel.
We design against it: the chatbot answers only from retrieved sources, shows them, and says so when the answer is not in the documents. We test it against real questions before launch and review failures afterwards.
A focused first version on one audience and a defined set of questions is usually working within a few weeks. Timelines for launch depend on integrations, sign-in and the review your security team needs.
Yes. Our published case studies include AI Sachiv, a citizen-facing assistant for Piramal Foundation adopted across Bihar, Jharkhand and Haryana, and a coordination chatbot for International Money Matters. We also run our own chatbot product, ChatEasy.
Tell us who the chatbot is for and the questions it should answer. We will come back with a practical view of scope, systems and where to start.