# 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

## 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.

## 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 a build actually goes

This is the inbound sales agent we built for a US B2B SaaS company, written up the way it happened. Yours won't go exactly like this, but the shape is usually close.

1. Wk 1  
Sat in on their inbound queue for three days. Demo requests landed in a shared inbox and waited about four hours for a reply, mostly because the SDRs were researching every lead by hand before writing back.
2. Wk 2  
Cut the scope to inbound demo requests. Partner enquiries and support questions come through the same form, and those stayed with people.
3. Wk 3–4  
Nothing shipped. We pulled a few hundred old leads out of HubSpot and had their best SDR mark the ones worth chasing. That became the test the agent had to pass.
4. Wk 5  
First version live, with a human in front of it. The agent qualified the lead and drafted the reply, and an SDR approved every email before it went out.
5. Wk 7  
Turned on booking, so qualified leads could pick a slot on a rep's HubSpot calendar straight from the first email.
6. Wk 9  
Median time to first reply was 6 minutes, down from 4 hours. We still run it with them.

## 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

## 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.

## 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.

Ideanab Private Ltd is a company registered in England and Wales, no. [16344048](https://find-and-update.company-information.service.gov.uk/company/16344048). Registered office: 124 City Road, London, EC1V 2NX, United Kingdom.

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