# Tekvo > Tekvo builds complete AI ecosystems for small and mid-size companies — the agents, the data underneath, and the systems around them. Live in weeks, in your cloud. Tekvo is an AI engineering studio. We build complete AI ecosystems — agent orchestration, retrieval and knowledge, memory engineering, evals and observability, and the data platform underneath — for companies of 20 to 500 people, and we run them in production. Positioning line: "From the dinosaur age to the agentic age." Contact: inquiry@tekvo.ai · Primary domain: tekvo.ai (tekvo.io redirects) · Founded 2018 Offices: Chennai IN, Nagpur IN, Toronto CA. ## What we sell - **TekPod** — a standing Tekvo team. We stand up GPU and cloud infrastructure in your account, build the agents for your use cases, and operate the system in production. One pod, one monthly price, no ML hires. Typical shape: 8–24 weeks, monthly. - **Scoping sprint** — two weeks, fixed fee, to decide whether an agent is worth building and what it costs to run. Ends with a go/no-go memo. - **Operate & evolve** — on-call, evals, model upgrades, and cost tuning on a rolling retainer. ## How we work - Measured before automated. Nothing is automated before it is baselined. - The eval harness is written before the agent. A golden set from real cases, scored, gates every release. - Autonomy is a dial, not a switch. Capability grants are explicit, logged immutably, and revocable in one call. - Your repo, your cloud. Everything runs in the client's account and lands in their repository from day one. - Median time from kickoff to a first agent in supervised production: 7 weeks. ## Key pages - [Home](https://tekvo.ai/): what Tekvo builds and for whom. - [Agentic Systems](https://tekvo.ai/agentic-systems): the agent lifecycle, guardrails, and where agents pay for themselves. - [Services](https://tekvo.ai/services): five disciplines, the stack we work in, and the three engagement models. - [Work](https://tekvo.ai/work): case studies with the numbers behind them. - [Company](https://tekvo.ai/company): who we are and what we hold to. - [Contact](https://tekvo.ai/contact): reply from an engineer inside one business day. - [Blog](https://tekvo.ai/blog) · [RSS](https://tekvo.ai/blog/rss.xml) - [Engineering notes](https://tekvo.ai/notes) · [RSS](https://tekvo.ai/notes/rss.xml) ## Case studies - [Refund resolution agent inside a payments ledger](https://tekvo.ai/work/refund-resolution-agent) — Fintech, 2026. 71% tickets auto-resolved; 4.2m avg handle time; 0 policy breaches. - [Retrieval platform over 2.1M clinical documents](https://tekvo.ai/work/clinical-retrieval-platform) — Healthcare, 2025. 94% citation accuracy; 1.1s p95 retrieval; 2.1M docs indexed. - [Exception-handling copilot for freight ops](https://tekvo.ai/work/freight-exception-copilot) — Logistics, 2025. 3.4× exceptions handled/day; −38% detention cost. - [Data platform rebuild ahead of a Series B](https://tekvo.ai/work/series-b-data-platform) — SaaS, 2024. <60s reporting lag; −54% warehouse spend. ## Writing - [The 25% of agent interactions that consume 75% of your budget](https://tekvo.ai/blog/agent-interactions-budget) — Where token spend actually goes once an agent touches real systems — and the four controls that flatten the curve. - [Restructure the work before you apply AI to it](https://tekvo.ai/blog/restructure-the-work-first) — AI compounds whatever process you point it at. Redesign first and it gets dramatically faster. Skip it and you run old bottlenecks at higher speed. - [Write the eval harness before the agent](https://tekvo.ai/blog/write-the-eval-harness-first) — A golden set built from real cases is the only thing that turns a demo into something you can deploy on a Tuesday. - [What we learned running a supervised agent for 14 months](https://tekvo.ai/blog/fourteen-months-supervised-agent) — Escalation rates, cost drift, model swaps, and the three incidents that changed how we grant capabilities. - [Cap agent spend on two axes, not one](https://tekvo.ai/notes/budget-ceilings-two-axes) — Per-run ceilings do not stop a loop that restarts. Cap per hour as well. - [Typed tools beat prompt-described tools](https://tekvo.ai/notes/typed-tools-beat-prompt-tools) — A short note on why we describe agent tools with schemas rather than sentences. ## Optional - [Sitemap](https://tekvo.ai/sitemap-index.xml) - [robots.txt](https://tekvo.ai/robots.txt)