Public-sector AI has to survive audit, handover and scale — not just produce a good demo. We consult on AI projects for government bodies across Odisha, structure them as proof-of-concept, pilot and scale, and build the officer capacity and documentation that let the work outlive the pilot.
Government AI fails in predictable ways: an unfunded pilot, a vendor who leaves, staff who never adopt the system, and no paper trail when the auditor asks. Our work is shaped against each of those, not against a technology wish-list.
We look at where the actual time and money go across a process, and name the specific problem worth solving — before anyone buys a tool.
We own the implementation through the hard middle where pilots stall, working alongside departmental teams rather than around them.
Odisha’s own AI investment has made Odia-language support a live requirement. We treat language, accessibility and last-mile usability as part of the design.
Most consultants leave after the report. We stay until the implementation returns value, at minimum cost, and then make sure your team can keep it running.
This mirrors the funnel Odisha’s AI Mission uses for solution providers — prove the idea cheaply, pilot it for real, then scale only what has earned it. Each stage has a decision gate, so a project that is not working stops before it becomes expensive.
We test the single riskiest assumption on a small, real dataset — fast and cheap. The output is evidence, not a presentation.
A controlled rollout in one office or service with real users. We measure against the original problem and document what adoption actually took.
Only after the pilot holds do we scale, with the documentation, SOPs and trained officers that a wider rollout needs.
We sit with the officers who run the process and map where time, money and goodwill leak. Public AI is a service-design problem before it is a technical one.
We agree the measurable outcome up front — turnaround time, error rate, citizen effort — so success and failure are both judged against the same yardstick.
Minimum cost to prove the point. If an off-the-shelf tool or a process fix beats a custom build, we say so — and where AI is not the answer, we say that too.
We manage the rollout through the middle where projects stall, and train the officers who will live with the system every day.
Documentation, SOPs and a trained team, plus our continued support through the first full cycle. Nothing depends on us being in the room.
An AI system nobody can operate is a liability, not an asset. Odisha’s own target is to train 75% of the state government workforce in AI by 2029 and 100% before 2036 — capacity is policy, not an add-on. We build it into every project rather than selling it separately.
For decision-makers: what AI can and cannot do, how to assess a proposal, and how to ask the questions that prevent an expensive mistake.
Training by role — officers, data staff, frontline teams — on the tasks they actually perform, not a computer-science syllabus.
Bias, privacy, consent and record-keeping, framed for public service and aligned to India’s data-protection expectations.
We equip a core of officers to train their colleagues, so capability spreads across offices without an external trainer in every room.
A role-based track that runs alongside the project, so officers are competent on the system before it goes live rather than after. We scope the hours against your department’s schedule and deliver in and around Bhubaneswar or remotely across Odisha. Ask about an officer training track.
Public money invites scrutiny. We make the paper trail part of the work, so a project can be defended in front of an auditor, transferred to a new officer, or repeated in another district without starting from zero.
Clear scope, deliverables and milestones, with the decisions and their reasons written down — the trail an audit needs to follow.
How data is collected, held and used, documented against India’s Digital Personal Data Protection Act, 2023, and stated plainly enough to explain to citizens.
The day-to-day procedures an office follows once the system is live, written for the officer who inherits it, not for the engineer who built it.
A structured handover to named officers, with the training and documentation required to keep the system running and improved after we step back.
We do not name a department, a partner or a project unless we have written permission to do so. These slots are where that work will appear once it exists and can be referenced. Until then they stay honestly empty.
Public-sector projects we are permitted to reference by name.
Public-sector engagements are listed here only where the department has agreed to be named.Time saved, cost reduced or errors removed, measured against the original problem.
Pilot results will be published with the sponsoring department's agreement.Departments and officers trained, and what they could do afterwards.
Officer-training numbers will be stated once departments clear them for publication.Officers or departments willing to speak to a prospective client.
References are provided directly, with the department's consent.Government buyers who want to check our approach are welcome to — get in touch and we will tell you honestly what we can and cannot yet show.
Tell us the service that is too slow, the paperwork that never ends, or the pilot you need to de-risk — and we will tell you honestly whether AI is the right answer, what a proof-of-concept would test, and what it would cost.