AI Engineering Company

AI that works
on day 100.

Koso is an AI engineering company. We build the evals, observability, routing, guardrails and retrieval systems that turn an impressive prototype into production software your CTO can sleep next to.

120+
AI systems in production
40-70%
Typical inference cost cut
<1%
Regression rate on ships
24/7
Observability on every agent

What we engineer

The layer
most teams skip.

A prototype is easy. A system that stays reliable while models, prompts, data and cost keep moving is engineering. That is the layer we own.

Evals

Evaluation Pipelines

Automated evals so every prompt change, model swap or data update is measured before it ships. Regression alarms wired to CI.

Observability

Tracing & Observability

Full trace of every agent run, tool call and token. Find failures in minutes, not by reading Slack threads.

Routing

Model Routing & Cost Control

Route to the right model for the job. Cache, batch and fall back cleanly. Cut spend 40 to 70% without losing quality.

Guardrails

Guardrails & Governance

Input/output filters, PII scrubbing, policy enforcement and audit trails. Safe for regulated data by default.

RAG

Retrieval Systems

Production RAG over your docs, tickets and databases. Chunking, embeddings, re-ranking, freshness and evaluations included.

Agents

Agent Infra

Tool schemas, memory, planners, retries, sandboxes. The plumbing that turns a demo agent into software you can trust.

Why Koso

AI engineering that
behaves like software.

CapabilityIn-house first attemptGeneralist dev shopKoso
Evals wired into CI, not spreadsheets
Full agent tracing and replay
Model routing with cost dashboards
Guardrails for regulated data
Production RAG with re-ranking + freshness
Ships to your repo, on your cloud
Same team from architecture to on-call

How we harden your AI stack.

Four phases. Audit and blueprint take two weeks each. Build lands in production in weeks. Operate stays as long as you want us on-call.

01

Audit

We inspect your current AI stack, traces, evals, cost curves and failure modes. You get a written report of what to fix first and why.

02

Blueprint

Target architecture: evals, tracing, routing, guardrails, retrieval, deployment. Signed off with your engineering leadership.

03

Build

Two-week ship cycles. Everything lands in your repo, on your cloud, wired into your existing CI, monitoring and on-call.

04

Operate

We run it with you. Watch cost, catch drift, refresh evals, ship model upgrades safely. Your team owns it end to end.

Stack

Opinionated on outcomes,
flexible on tools.

We work with the LLM providers, vector stores, observability tools and clouds your team already trusts. If you don't have a preference, we bring a boring, battle-tested default.

OpenAIAnthropicMistralLlamaLangGraphVercel AIPineconepgvectorSupabasePostgresRedisCloudflareKubernetesDockerGitHub ActionsDatadogOpenTelemetryGrafana

Trust

An engineering team that has run this at scale.

120+ AI systems in production

Across ops, sales, support, product, education and content. All still shipping.

Evals-first by default

No prompt change ships without a passing eval. No model swap without a benchmark.

Boring where it matters

Predictable cost, predictable latency, predictable behaviour. Excitement in the outputs, not the outages.

Questions

Before you ask.

How is an AI engineering company different from an ML team?+

Classic MLOps is about training and serving models you own. LLM engineering is about wrapping foundation models with the eval, observability, routing and safety layers that make them behave like software. We do the second and, when needed, the first.

Do you replace our engineers or work alongside them?+

Alongside. We embed with your team, ship code into your repo, review PRs and hand over ownership as we go. If you have no AI engineers yet, we can also run it end to end until you hire.

Which stacks do you work with?+

Any modern web stack. Python or TypeScript backends, Postgres or vector DBs, whatever cloud you run. We are opinionated about evals and observability, agnostic about the rest.

Can you fix an AI system built by someone else?+

Regularly. Rescue engagements start with a 1-2 week audit: traces, evals, cost, failure modes. You get a written plan and a fixed price to bring it up to production standard.

Where does your team work?+

Distributed across India, the US, UK and Australia. Async by default, on-site when it helps ship faster.

Make your AI boring in the best way.

Send us your traces, your bill or your prototype. We come back in 48 hours with a scoped plan to make it reliable, observable and cheap.