# AI Engineering Company | Evals, Observability & LLM Infra | Koso

> Koso is an AI engineering company that builds the unglamorous layer most AI projects skip: evals, observability, prompt versioning, model routing, guardrails and cost control. Production-grade AI that works on day 100.

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 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.

[Book an engineering call ](/book)[See what we engineer](#capabilities)

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.

| Capability                                 | In-house first attempt | Generalist dev shop | Koso |
| ------------------------------------------ | ---------------------- | ------------------- | ---- |
| 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.

[Book an engineering call ](/book)[See AI consulting](/ai-consulting-company)

A custom AI development agency. Bespoke agents, copilots and products engineered for teams that want a moat, not a subscription.

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