Services · Artificial intelligence · Enterprise AI

A private AI assistant that knows your company data.

Q: How do you stop people seeing documents they should not?

The assistant applies the source system permissions on every query. When a user asks a question, the search step only retrieves content that user can already open in SharePoint, Dynamics or the ERP. We test this with accounts at different permission levels before launch, and the checks are logged.

Q: Can the assistant give wrong answers?

Yes, so every answer shows its source. Retrieval reduces made up answers but does not remove them. We show the documents behind each answer, tell the assistant to say when it cannot find something, and track quality against an evaluation set. Staff are trained to check sources for anything important.

Q: Do we need Microsoft 365 Copilot?

No, but we can build on it if you have it. A private assistant can run as its own web app or inside Teams without Copilot licences. If you already use Copilot, extending it with Copilot Studio is often the quicker route for Microsoft centred companies.

Q: Where is the data stored?

In your cloud tenant, in the region you choose. Indexes, logs and configuration live in your Azure or AWS subscription. The design follows GDPR and the data protection law in each market and your internal data classification rules, and your team holds the encryption keys.

Q: How do we keep the assistant up to date?

Sources are re indexed automatically on a schedule. New and changed documents are picked up on a set schedule or when they are saved. Removed documents drop out of the index. We monitor indexing jobs and alert your team if a source stops syncing.

Codeeo builds enterprise AI solutions that let staff ask questions of your own documents and systems without sending data to public tools. Assistants run in your cloud tenant, sign in through your existing identity provider and only show people what they are already allowed to see.

The result is one place to find policies, contracts, customer records and procedures, with a source shown for every answer.

Get in touch All AI services

Built for: IT heads and operations leaders at larger organisations that need AI inside their security, identity and data rules.
Typical timeline: A first assistant over one or two data sources typically runs in a few weeks, with more sources and departments added in later phases.
What you own: The deployment sits in your own Azure or AWS tenant, and you own the code, indexes, prompts and configuration.
Rated: 4.7 / 5 from 23 Google reviews

200+ companies across the US and the UAEFrom a first store to a listed developer

What you get

What an enterprise AI build includes.

Enterprise work is less about the model and more about permissions, data sources and the controls around them.

Explain your project

Private LLM deployment
We deploy language models through Azure OpenAI, AWS Bedrock or self hosted open models such as Llama and Mistral. Traffic stays on enterprise endpoints that do not train on your prompts.
Staff get modern AI without company data leaking into consumer chat tools.
Search over company data
We build retrieval augmented generation over SharePoint, OneDrive, Dynamics 365, ERP records and file shares. Documents are chunked and indexed in a vector database such as pgvector or Pinecone, and every answer cites its source.
People find the right policy or record in seconds and can check where the answer came from.
Single sign on
Assistants sign users in through Microsoft Entra ID, Okta or your existing identity provider. There are no separate passwords and leavers lose access when their account is disabled.
IT manages AI access the same way it manages everything else.
Role based access
The assistant checks document and record permissions at query time, using the same rights as the source system. A sales rep and a finance manager asking the same question can get different answers.
Confidential HR, finance and legal content stays with the people cleared to see it.
Your own cloud tenant
We deploy into your Azure or AWS subscription with your network rules, key management and region choices. Your security team reviews the architecture before go live.
Data residency and security decisions stay under your control.
Microsoft Copilot extensions
We build Copilot Studio agents, plugins and connectors that bring your line of business systems into Microsoft 365 Copilot and Teams. Staff ask in the tools they already use.
You get more from Copilot licences you already pay for.
Assistants in daily tools
Assistants can sit in Teams, a browser extension, your intranet or an internal web app. We design the interface around the tasks staff actually perform, such as drafting replies or checking a contract clause.
Adoption is higher because nobody has to learn a new place to work.
Evaluation and audit logging
We test assistants against an evaluation set of real questions before launch and after every change. Every prompt, answer and source is logged for review and audit.
You can show what the assistant said, why, and how its quality is being checked.

Who this is for

Common starting points for enterprise AI.

Most enterprise clients come to us with one of these three problems.

Staff are pasting data into public tools

People are already using consumer chat tools with company documents because nothing better exists. A private assistant gives them the same convenience inside your controls, so you can enforce a sensible policy.

Replace shadow AI.

Knowledge is spread across systems

Answers live in SharePoint, the ERP, email and the heads of long serving staff. An assistant with search across these sources gives new and existing staff one place to ask.

Search across sources.

You own Copilot and want more from it

Microsoft 365 Copilot is rolled out but cannot see your line of business data. We build extensions and agents that connect it to Dynamics 365, your ERP or custom systems.

Extend Copilot.

How we deliver an enterprise assistant.

01ScopeAgree the users, the data sources, the permission model and the questions the assistant must handle.
02ArchitectDesign the deployment in your tenant and review it with your security and data teams.
03BuildIndex the sources, wire up identity and permissions, build the interface and test against the evaluation set.
04Roll outLaunch to a pilot group, collect feedback, then widen access department by department with monitoring in place.

How we work together

Built in phases, owned by you.

We usually start with one department and one or two data sources, then add more once the first group relies on it.

Discovery and pilot

Teams deciding where AI should start

A short, fixed piece of work: the use cases ranked, the data checked, and one pilot built against real examples so you can judge it on evidence rather than a demo.

Use cases ranked by value and risk
Data and systems review
A working pilot on your own examples
A written plan for what comes next

Get in touch

Build and launch

A use case that is ready to go live

A written scope and a fixed quote, then the build: integrations, guardrails, an evaluation set and a launch with your team trained to use it.

Written scope and acceptance tests
Integration with your systems
Evaluation set and human review steps
Launch, training and handover

Get in touch

Run and improve

AI that is live and needs looking after

A monthly plan to watch quality, cost and usage, update prompts and models as vendors change them, and add the next use case when the first one earns it.

Quality and cost monitoring
Model and prompt updates
A monthly report on usage and results
The same team that built it

Get in touch

Technology

What we build this with.

Models are chosen per use case for quality, cost and where your data is allowed to go. The systems around them run on the stack below.

01Web

React
Next.js
TypeScript
Node.js
Laravel
WordPress

02Data and cloud

PostgreSQL
MySQL
MongoDB
AWS
Azure
Docker

Questions about enterprise AI.

Is our data used to train the model?

No. We use enterprise endpoints or self hosted models.

Azure OpenAI, AWS Bedrock and the enterprise plans of model providers do not train on customer prompts under their standard terms, and your security team should review those terms. For the strictest cases we can self host an open model such as Llama or Mistral entirely inside your network.

How do you stop people seeing documents they should not?

Can the assistant give wrong answers?

Do we need Microsoft 365 Copilot?

Where is the data stored?

How do we keep the assistant up to date?

Services that usually go with this one.

AI integrationAdd AI to the systems you already run.AI security, risk and governanceUse AI with controls you can explain to an auditor.Microsoft Dynamics 365Sales, service and finance on one Microsoft platform.

Tell us what the business needs. We will tell you what to build first.

Schedule a free consultation and get a written recommendation, with costs, before you commit to anything.

Get in touch

PROVEN RESULTS

4.7 / 5from 23 Google reviews

A private AI assistant that knows your company data.

What an enterprise AI build includes.

Private LLM deployment

Search over company data

Single sign on

Role based access

Your own cloud tenant

Microsoft Copilot extensions

Assistants in daily tools

Evaluation and audit logging

Common starting points for enterprise AI.

Staff are pasting data into public tools

Knowledge is spread across systems

You own Copilot and want more from it

How we deliver an enterprise assistant.

Built in phases, owned by you.

Discovery and pilot

Build and launch

Run and improve

What we build this with.

Questions about enterprise AI.

Services that usually go with this one.

Tell us what the business needs. We will tell you what to build first.