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Agents, copilots and RAG that actually ship — the useful kind, not the demo that dies on Monday. Plugged into your real data.

AI

AI is having a moment, and most of it is demos that die after the meeting. We build the useful kind — agents, copilots and retrieval grounded in your real data, with the evaluation to prove it works before it touches a customer.

It reads your docs so it doesn't have to guess.

Retrieval-grounded, source-cited, and measured against an eval set before it ever meets a customer.

Question“What’s our refund window?”
Retrieve3 docs from your data
refunds.mdterms.pdffaq.md
Reasongrounded + cited
Answerready for a customer
UserWhat’s our refund window?
Copilot

Capabilities

The useful kind of AI

Grounded copilots

Support and internal assistants that answer from your actual docs (RAG), with sources attached.

Agents that act

Not just chat — agents that take real, permissioned actions in your systems.

Document processing

Extraction, classification and summarisation across the paperwork that slows you down.

In-product AI

LLM features built into the product you already ship to customers.

Evaluation harnesses

A measured accuracy score before launch — and tracking once it’s live.

Guardrails & handoff

Citations, fallbacks and human handoff so it’s safe in front of customers.

We don't ship demos that die on Monday. We ship the boring, useful kind.

Architecture

How an answer is made

Every response is retrieved from your data, reasoned over by a model, and logged so you can measure it.

Input
User questionUploaded files
Retrieval
EmbeddingsVector searchTop-k docs
Reasoning
Claude / GPTToolsGuardrails
Output
Cited answerHuman handoffEval logging

The process

How it works

01
Step 01

Find the real use case

We start with a job worth doing — not 'add AI'. The boring, high-volume task wins.

02
Step 02

Prove it on your data

We prototype against your real documents and measure it with an eval set.

03
Step 03

Ship with guardrails

Citations, fallbacks and human handoff, so it's trustworthy in front of customers.

04
Step 04

Monitor & improve

We track accuracy in the wild and tune as your data and needs change.

The difference

Demo-ware vs. shippable AI

The usual way

  • A demo that wows once, then makes things up
  • Answers with no source you can check
  • Locked to one vendor’s model and pricing
  • “Add AI” with no way to measure it

With MODZ

  • Grounded in your data, cited and measured
  • Every answer links back to a source
  • Model-agnostic — we pick on accuracy and cost
  • An eval score before it ever ships

By the numbers

What good ai buys you.

100%
of answers cited to a source
Eval-first
measured before it ships
Any model
Claude, GPT or open — your call

The toolkit

ClaudeOpenAIRAGEmbeddingsEval harnessesGuardrails

Working together

No account managers. No markup. No mystery.

We start with the job

Bring the boring, high-volume task. If AI isn’t the right tool, we’ll tell you — sometimes plain automation wins.

Proven on your data

We prototype against your real documents and measure it with an eval set before launch.

Watched in the wild

We track accuracy once it’s live and tune as your data and needs change.

Questions

Frequently asked

Won't it just make things up?

That's why we ground answers in your actual data and cite sources, then measure it with an evaluation set before launch. If it's not accurate enough, it doesn't ship.

Is our data safe?

Yes. We design around your privacy requirements, keep your data in systems you control where needed, and never train public models on your private content.

Which AI models do you use?

Whatever fits the job and budget — we're not tied to one vendor. We pick on accuracy, cost and your data constraints, and can swap as the landscape moves.

Is this actually worth it for us?

Only if there's a real, repetitive task it can take off your plate. We'll tell you honestly if AI isn't the right tool — sometimes plain automation does the job better and cheaper.

Proof

AI we've shipped

Ready when you are

Let's build the useful kind.

Bring the repetitive, high-volume task. We'll prove it on your real data — or tell you honestly when AI isn't the answer.