Move from stalled pilots to production-grade AI systems with measurable business impact. eonai architects, stress-tests and operates enterprise agents and AI workflows built for real customer traffic, strict security perimeters and predictable unit economics.
Start from a golden test set built out of real production edge cases, then fix retrieval, tool calls and model routing until the system clears that bar consistently.
Groundedness & evaluation playbook →Wire a layered evaluation suite into CI so every prompt, model or schema change is scored on accuracy, latency and cost before the pull request merges.
5-layer CI evaluation playbook →Move permissions, spending caps and PII rules out of the system prompt and into deterministic server code, backed by an audit trail reviewers can inspect.
Transactional guardrails playbook →Automate routine triage, extraction and resolution end to end, hand exceptions to a specialist with full context attached, and measure the unit cost removed.
Multi-agent orchestration playbook →Generate and maintain unit, API and UI tests from code diffs and Jira specs, quarantine flaky tests automatically, and gate releases on traceable evidence.
Explore assuregraph →Embed an experienced fractional CTO for one to three days a week, or stand up a dedicated India engineering pod, to own architecture, hiring and delivery cadence.
Fractional CTO & India pods →Six scenarios that read as ordinary customer traffic and cost money, and the seven architectural controls that close them.
Read →An upgrade that improved decisions while making 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 →How engineering teams modernise testing, coverage and release gates as software delivery accelerates.
Watch on YouTube → (opens in a new tab)Agents that execute real transactions, knowledge assistants that verify every citation, and document workflows built for messy operational data.
Measure accuracy, safety and cost before customers find the gaps. Adversarial testing for live agents, layered evaluations in CI, and automated test maintenance.
BCG estimates 10% of AI’s value comes from the model, 20% from data and technology, and 70% from how workflows, governance and teams change. That 70% is where pilots succeed or stall.
Senior technology leadership without a six-month executive search, and dedicated AI and quality engineering teams set up and run in India.
Bind sessions to verified identity, enforce cumulative refund caps in server code, and hand ambiguous or high-value cases to a person.
If your agent can issue refunds, a content filter is not protecting you →Answer staff or customer questions from internal policies, contracts and manuals, checking every citation against the source passage before a reply goes out.
A fluent answer is not a grounded answer →Extract, cross-check and reconcile fields across noisy PDFs and forms at volume, using a deterministic orchestrator so the model never improvises a payout rule.
Keep the orchestrator deterministic →Watch commits, Jira specs and logs across fast-moving repositories, and open pull requests with the unit or API tests each change requires.
Explore assuregraph →Link customer, vendor or claimant records across noisy datasets, including transliterated Indian-language names, with calibrated thresholds for human review.
Pre-screen documents and decisions against policy, strip prompt injections from uploaded files, and record the rubric breakdown behind every decision.
Fairness is a test you run before release →Send narrow classification steps to fast, inexpensive models and save frontier models for complex reasoning, with a full log of every routing decision.
Patent pendingBring an architecture diagram or a stalled pilot for a candid technical view on where the risks sit.
Describe the problem →Most work begins with a short, fixed-scope engagement that ships a working asset and evaluation suite. Select the track that matches your current stage below.
Start with a 2-week discovery and readiness sprint, build and evaluate the live agent in 4–6 weeks, then run continuous regression and drift checks monthly.
6–8 weeks to productionBenchmark the live system against adversarial edge cases and wire an automated evaluation suite into CI, then keep drift and regressions covered monthly.
2–3 weeks to release gatesShip the core AI product on real data in 4–6 weeks while a fractional CTO owns architecture, early engineering hiring and investor diligence alongside.
4–6 weeks to live productUse-case discovery, data feasibility checks and a prioritised 90-day roadmap for enterprises starting with AI.
A working agentic system running on real data inside a private cloud environment, shipped with its evaluation suite.
An independent red-team and reliability assessment of an AI product already running in production.
Ongoing evaluation runs, drift monitoring and release control after launch, for teams that want predictable production operations.
Senior technology leadership on a predictable weekly cadence for Seed to Series A startups.
A diagnostic of testing and release practices for engineering teams shipping at high velocity.
Hands-on working sessions for engineering, product and risk teams, run on real internal systems rather than toy examples.
End-to-end production builds, multi-quarter transformation programmes and dedicated India capability centres (5–50 engineers), scoped after a short discovery phase.
Deployments run inside the client VPC or on-premise environment. Commercial model APIs are configured with zero data retention or training, and self-hosted open-weight models are used whenever data residency rules demand it.
Every line of application code, prompt template, golden dataset, evaluation harness and runbook transfers in full on delivery. Mutual NDAs are signed before any internal architecture is shared.
No reseller deals with cloud or model vendors. OpenAI, Anthropic, Google, Llama and Mistral are benchmarked on real tasks across AWS, Azure, GCP, OCI or bare metal, selected purely on measured accuracy, latency and cost.
Least-privilege tool scopes, tamper-evident decision logs and human-in-the-loop gates are wired in from day one, aligned with the NIST AI RMF, the EU AI Act’s high-risk tiers, India’s DPDP Act and GDPR.
eonai brings rigorous quality engineering discipline to enterprise AI delivery.
Founded by engineers who have built and operated high-reliability software for millions of users at global technology companies, eonai treats AI systems with the same production standards as core financial and operational infrastructure.
Every engagement starts with a clear definition of success, a test suite drawn from real operational edge cases, and an architecture built for the client’s security perimeter. Alongside custom engineering, eonai builds assuregraph, an autonomous quality engineering platform that keeps test coverage and release evidence aligned as software changes.
Straight answers on timelines, stack independence, production safety, IP handover and commercial structure.
Send over the context and we will address it directly.
Ask a question →Usually within one to two weeks of the first conversation. Fixed-scope engagements like the AI Opportunity Sprint or the AI Reliability Audit kick off as soon as a statement of work is signed and read access to the relevant systems is granted. There is no hand-off from a sales team to a delivery team.
Yes. Engagements run remotely with engineering and leadership teams across the United States, the United Kingdom, Europe and Asia-Pacific, aligned with local working hours. Contracts are available under Indian or local jurisdiction, invoiced in USD, GBP or INR, with on-site travel for kickoffs and key milestones on longer programmes.
Whichever fit the accuracy, latency and data-governance requirements. eonai is independent of every model vendor and cloud provider, working routinely with OpenAI, Anthropic, Google, Llama and Mistral models across AWS, Azure, Google Cloud, OCI and on-premise environments, including fully self-hosted deployments.
Turning safety and accuracy into executable tests before writing application code. Every system ships with a golden evaluation set drawn from real operational data, adversarial probes for prompt injection and policy bypasses, deterministic guardrails enforced in code, and human-in-the-loop checkpoints wherever an action moves money or alters records.
An AI Reliability Audit. Over two to three weeks, the live system is benchmarked on real edge cases to expose where standard filters or prompts fall short, producing a prioritised fix plan alongside an automated test suite that runs on every release. For ongoing protection against drift afterwards, Managed AI Operations covers that monthly.
The goal of every engagement is to leave the internal team able to run, test and extend the system independently. Most organisations bring eonai in to get the first critical system into production with a solid evaluation harness, then hire permanent engineers against a working reference architecture instead of a blank sheet of paper.
eonai builds high-bar engineering and quality pods in India from the ground up for global companies, handling technical bar-raising, interviewing, onboarding, tooling and the operating rhythm that keeps a distributed team tightly aligned with product leadership. The sweet spot is teams of 5 to 50 for startups and mid-market companies, a size giant outsourcing firms rarely serve well.
Fixed price for defined engagements (AI Opportunity Sprint, Agent MVP, AI Reliability Audit, QE Health Check, Team Workshops); a predictable monthly retainer for Managed AI Operations and Fractional CTO leadership; and time-and-materials for larger multi-quarter builds. Every quote spells out the exact deliverables and timeline upfront.
Bring a stalled pilot, an architecture question or a live agent that needs hardening. Connect with eonai for a 30-minute technical working session, not a sales pitch.