AI agents that survive production.
Kevadia builds production AI automation for enterprises — workflow agents, RAG systems over private data, copilots, and the eval harnesses that measure them. Most enterprise AI dies between the demo and the deployment; we build in the opposite order, production constraints first.
FIG. 02 — AI Automation & AgentsEnterprises don't lack AI ideas. They lack AI that holds up.
Pilots stall because agents hallucinate against real data, break on edge cases nobody scripted, and give you no way to tell whether they're improving or quietly degrading. Crossing from prototype to dependable system is an engineering problem, not a prompting problem.
THE OPERATING SEQUENCE.
- Phase 01
Ground truth
We map the workflow end to end, define what correct means in your context, and assemble the examples the agent will be judged against. If correctness can't be defined, the project isn't ready.
- Phase 02
Agent architecture
Orchestration, tool use, retrieval over your private data, permission boundaries, and human-in-the-loop checkpoints — designed before the first prompt is written.
- Phase 03
Eval harness
Every behavior gets a regression suite. Changes ship when the evals pass, not when the demo feels good.
- Phase 04
Hardened deployment
Tracing, cost controls, fallback paths, and staged rollout — so the agent earns autonomy instead of being granted it.
WHAT WE BUILD.
Claude, GPT, and open-weight models where data can't leave your perimeter; retrieval on pgvector, Pinecone, or OpenSearch with hybrid search and reranking; deployed on AWS, GCP, or Kubernetes.
FREQUENTLY ASKED.
What is an enterprise AI agent?
An enterprise AI agent is software that uses a large language model to complete multi-step business tasks: reading context, calling tools and APIs, and acting within defined guardrails. Unlike a chatbot, an agent owns an outcome — resolving a ticket, reconciling a record — not just a conversation.
How long does it take to build a production AI agent?
A focused production agent typically takes six to twelve weeks: roughly two to map the workflow and define evals, four to six to build and iterate, and the remainder hardening for deployment. Timelines stretch when data access is unresolved, so we settle that in week one.
How do you prevent hallucinations?
Structurally, not with prompt wording. Agents retrieve from your verified data instead of relying on model memory, cite their sources, refuse when confidence is low, and are tested against regression suites that measure factual accuracy before and after every change.
Should we buy an off-the-shelf AI tool or build?
Buy when a vendor's workflow matches yours exactly; build when the agent must work inside your data, permissions, and edge cases. Most of our engagements start where an off-the-shelf pilot stalled.
Which models do you use?
We're model-agnostic and benchmark against your actual tasks: frontier models like Claude or GPT for reasoning-heavy work, smaller or open-weight models where latency, cost, or data residency demand it — with an eval harness that makes switching models a measured decision.