Plenty of AI pilots look good in a meeting and quietly stall afterwards. We help startups and enterprises get past that point: building, testing and running AI systems their customers, engineers and auditors can rely on.
EonAI’s engineers spent twenty years keeping software reliable for millions of users before turning that discipline to AI. Hands-on, direct, and honest about what AI can and can’t do yet.
A working agent in weeks, senior technical leadership for the days you need it, and the evaluation evidence investors and first customers ask for.
Book a working session →A clear view of where AI pays off, governance your risk and compliance teams will sign off on, and delivery that fits how your engineers already work.
Request an AI readiness workshop →We rebuild around your actual data and edge cases, starting with a test set that defines what “working” means before any code is written.
We build an evaluation suite that runs on every change, so a drop in accuracy, safety or cost shows up before your customers notice it.
We design the controls, audit trails and human checkpoints regulated teams expect, and document them in the language your reviewers use.
We automate the classification, triage and first responses, with AI that knows when to hand off to a person, and we measure the cost it takes out.
We bring your quality process up to speed for AI-generated code: automated test generation, risk-based coverage and release gates that protect velocity without the outages.
A fractional CTO who owns architecture, hiring, delivery and AI strategy for the days a week you actually need.
AI agents that complete real tasks, assistants that answer from your own documents and data, and automation for support and operations.
Find out how your AI really performs before your customers do. We measure accuracy, safety and cost, look for the ways it fails, and keep it dependable in production.
The technology is the easy part. BCG estimates only about 10% of the value from AI comes from the model, 20% from data and technology, and 70% from how work, governance and teams change around it. That 70% is what we help with.
Senior technology leadership without the full-time hire, and engineering or QE teams set up for you in India. Large firms do this for large companies; we do it for startups and mid-market teams of 5 to 50.
Problems we have built for, tested or led before, so we start with a working pattern rather than a blank page.
Classify, route and draft replies to tickets and complaints, with a person in the loop for the difficult ones.
Answer staff or customer questions from policies, contracts and manuals, citing the source every time.
Advisory note: a fluent answer is not a grounded answer →Extract, check and reconcile data from invoices, KYC files, claims and forms at volume.
Advisory note: keep the orchestrator deterministic →Deduplicate, prioritise and route bugs, alerts and test failures to the right team without manual sorting.
Generate and maintain tests, flag risky changes and shorten regression cycles in fast-moving codebases.
Link records across messy datasets, including Indian-language names, into one trusted view.
Pre-screen documents and decisions against policy, flag exceptions and keep a full audit trail.
Advisory note: guardrails belong in the architecture →Send each task to the model that handles it best on accuracy and cost, with a record of every decision.
*Results achieved by EonAI’s leadership in prior roles at a global mobility platform, a gaming company, a fintech and a national data programme. Your results will depend on your data, scope and starting point.
Describe your problem →Think of an AI agent as a new hire. It needs a job description, a probation period, performance reviews and an exit process. That is what evaluation-first delivery means in practice: we agree what “good” looks like before writing any code, then test against it at every step. You see the numbers as we go, not just a demo at the end.
Choose the use case, agree the business metric and build the test set the system has to pass. The job description.
A working version on your data within weeks, using whichever model handles each task best on accuracy and cost.
Measure accuracy, safety and cost. Try to break it. Add guardrails until it clears the bar you set. The probation period.
Deploy with monitoring and train your team, so the momentum stays with you after we leave. Code, prompts, tests and documentation are all yours.
Most relationships begin with a short, fixed-scope engagement. Each one ends with something concrete you keep.
Use-case discovery, feasibility checks and a prioritised roadmap, for enterprises starting with AI.
A working agentic AI system on your own data, for startups and innovation teams.
An independent evaluation of an AI product you already run, for teams shipping AI today.
An assessment of your testing and release practices, for scale-ups with quality problems.
We keep your AI working after launch, for teams who would rather not build an AI operations function yet.
Senior technology leadership on a retainer, for seed to Series A startups.
Hands-on sessions for engineering and product teams, in person or remote.
Full builds, multi-quarter transformations and India capability centres are scoped individually after a short discovery phase.
Start with a conversation →“AI projects are successful only if they keep working after they are deployed.”
QuantumBlack, AI by McKinsey, on the AI operations challenge. It is why Managed AI Operations is on this list.
We build inside your cloud or on your infrastructure. Where a third-party model is used, it is accessed under business terms that exclude training on your data, or replaced with a self-hosted model where your policy requires it.
Code, prompts, evaluation suites and documentation are yours on delivery. We sign a mutual NDA before any detailed discussion.
OpenAI, Anthropic, Google, Llama, Mistral or open-source models; AWS, Azure, Google Cloud, OCI or on-premise. Chosen per task on accuracy, cost and your data policy.
Audit trails, access controls and human checkpoints built in from the start, designed to the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act’s risk tiers, India’s DPDP Act and GDPR.
We don’t start from a blank page. Two pieces of our own work shorten engagements and raise reliability, and you can use them without being locked into them.
Routes each task to the best-fit model for accuracy and cost, with a compliance trail for every decision. Patent application filed in India, 2025.
Evaluation, test generation and triage for AI systems and AI-written code, in development on the engine above. The tooling behind our Assure work.
EonAI is an India-registered firm serving clients worldwide. Its leadership spent two decades keeping software reliable for millions of users at global technology companies before turning that discipline to AI. The firm is senior-led and hands-on: the people who scope an engagement are the people who deliver it.
We are not an outsourcing firm. We are the people you call when the AI has to work.
Short advisory notes on how agentic systems fail in production and the controls that prevent it, written for the person accountable for the decision.
Six scenarios that read as ordinary customer traffic and cost money, and the seven controls we recommend.
Read →An upgrade that improved decisions and quietly made the agent slower and less consistent. One accuracy number would have hidden both.
Read →A knowledge assistant can pass every task check and still answer from nothing. What to measure before it reaches your clients.
Read →
A guest talk by EonAI’s CTO on what changes in testing and release practice when much of your code is written by AI.
Watch on YouTube →Usually within one to two weeks of a first conversation. Fixed-scope engagements such as the AI Opportunity Sprint or the AI Reliability Audit begin with a short kickoff, access to the relevant data or systems, and a signed statement of work. The people who scope the engagement are the people who deliver it, so there is no hand-off between a sales team and a delivery team.
Yes. We work remotely with teams in the United States, the United Kingdom, Europe and Asia-Pacific, and arrange working hours to overlap with yours. Contracts can be under Indian or your local jurisdiction, and we invoice in INR, USD or GBP. For longer programmes we can travel for kickoffs and key milestones.
Whatever fits your constraints. We are not tied to any model provider and routinely work with OpenAI, Anthropic, Google, Llama and Mistral models, choosing per task on accuracy, cost and your data policy. We deploy on AWS, Azure, Google Cloud, OCI or on-premise, and we can use self-hosted models where data must not leave your environment.
By agreeing what “good” looks like before we build, then measuring against it. Every system ships with an evaluation suite that scores accuracy, safety and cost on your real data, adversarial tests that try to make it fail, guardrails for the failure modes we find, and human checkpoints where the stakes are high. You see the results, not just a demo.
Start with an AI Reliability Audit. In two to three weeks we measure how the system actually performs, find where it fails, and give you a prioritised fix plan plus a re-runnable test suite your team owns. If you then want someone to keep it healthy as models and prompts change, Managed AI Operations covers that on a monthly basis.
Both, usually. Our goal is to leave your team able to run and extend what we build, not to make you dependent on us. Every engagement includes knowledge transfer, documentation and the evaluation tooling in your hands. Many clients use us to get the first system into production quickly, then hire against a working example rather than a blank job description.
Yes. We have built engineering and quality organisations in India from scratch for global companies, including hiring, onboarding, tooling and the operating cadence that keeps a remote team aligned with headquarters. We focus on teams of 5 to 50 for startups and mid-market companies, a segment the large capability-centre providers generally don’t serve.
A fixed price for defined engagements such as the Sprint, the Audit and the Health Check; a monthly retainer for Managed AI Operations and Fractional CTO work; and time and materials for longer builds and programmes. We quote after a short scoping conversation, and the quote includes what you will walk away with and when.
If you’d rather talk, book a 30-minute call with a senior engineer. It’s a working session on your problem, not a sales pitch.