Every claim carries its evidence
Citations, receipts, epistemic markers — in our product and in what we build for you.
A PRODUCT AND ENGINEERING COMPANY
Engineering you can inspect. Software you can trust.
Applied AI engineering for teams that need production, not pilots.
We deliver purpose-built digital solutions for organizations that demand technical rigor and absolute reliability. Our craftsmanship meets high-precision laboratory standards.
LLMs narrate. They never decide.
OUR DOCTRINEDeterministic where it matters
Thresholds, severity and state are computed, not generated.
You can operate it without us
Documentation, hand-over, open standards.
How we deploy.
Applied AI
Embedded senior engineers who ship AI into production, not pilots.
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The data foundation and runtime that make AI dependable.
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Solving business challenges with a combination of technology and domain knowledge.
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Senior AI pods inside your team, at a fraction of US cost.
Learn moreShipped, measured.
All workANVEEKSHA · PRIVATE PREVIEW
What we’re building.
Anveeksha connects the tools your teams already work in (project tracking, documentation, code, team chat: Jira, Confluence, GitHub, Slack, etc.) into one stream of activity, and works out who did what across all of them. It then spots what has shifted against each project's own normal: work that has gone quiet, sudden surges, plans that have drifted, cost and schedule slipping, and people or knowledge a project quietly depends on too heavily. Each change is explained in plain language, and every sentence points back to the evidence behind it. The AI writes the explanation; it never decides how serious something is. We run Anveeksha on Anveeksha, and it already shows us things about our own delivery that we had not seen.
Join the waitlistLatest thinking.
All postsFinished in code, still "to do" on the plan.
Anveeksha compares what happened in GitHub with what Jira reports. On our own project it found 49 pieces of work merged in code while the plan still said they were not done. Half had waited more than two weeks, eight for over a month, one for 97 days, and every report built on that plan was wrong for as long as the gap stood. Here is how it spotted them and what it showed as evidence.
Anveeksha team · · 3 min read
Designing RAG systems that survive production
Retrieval-augmented generation is well understood in research settings. In production the failure modes compound quietly: stale indexes, embedding drift, context poisoning and latency cliffs. These are the safeguards we put in place before a retrieval system ships.
Anveeksha team · · 8 min read
95% OF AI PILOTS SHOW NO MEASURABLE P&L IMPACT — MIT NANDA, 2025.