Give your company its own AI capability
Important knowledge lives in documents, inboxes, and people’s heads, and answers depend on who happens to be available.
What this means
A custom AI system is built around your documents, data, rules, and permissions: a private assistant, an internal copilot, or an AI feature inside software you already run. The architecture is chosen from the requirements (model, retrieval, evaluation, review) rather than assumed. This does not necessarily mean training a foundation model from scratch. It means selecting and wiring the right models behind an adapter layer, grounding them in your material, and proving the result before it touches real work.
Problems this solves
- Questions that require digging through policies, contracts, procedures, or old email threads
- Knowledge that disappears when one person is unavailable
- A product or service that needs an AI capability, not a generic chatbot
- Teams experimenting with AI tools individually, with no controls, evidence, or shared standard
Systems we build
- Private company knowledge assistants over internal documents
- Retrieval systems that cite the source beside every answer
- Department-specific copilots for finance, operations, or service teams
- AI embedded into an existing application or portal
- Model selection and evaluation for a specific workload
- Role-aware assistants that respect per-team permissions
- Company-specific LLM workflows for drafting, review, and routing
- Multimodal systems where documents and images carry the meaning
- Your documents + data
- Permission-aware retrieval
- Model behind an adapter
- Evaluation + citation
- Human review
- Answer in your systems
Who this is for
- Operating companies whose knowledge is spread across documents and systems
- Product teams adding an AI feature to software they already sell or run
- Finance and operations leaders who want AI with evidence, not demos
- Organizations that need model use contained inside their own environment and permissions
Common integrations
- Microsoft 365, SharePoint, and file stores
- Internal document repositories and databases
- ERP, accounting, CRM, and ticketing systems
- Model providers behind an adapter layer, or a model endpoint you host
- Single sign-on and existing permission models
How an engagement works
- 01Define the real questions, users, and source material before any model is chosen
- 02Build an evaluation set from your documents and the answers your team considers correct
- 03Implement retrieval, model routing, review, and logging as one system
- 04Roll out to a limited group first, then widen access once the evidence holds
Controls and validation
- Every answer cites the material it came from, or says it does not know
- Retrieval respects the permissions of the person asking
- Evaluation runs against your data before rollout, and monitoring continues after
- Model providers are used behind an adapter layer, with data handling terms reviewed first
What you receive
- A working assistant or AI feature in your environment
- Evaluation results on your own documents and questions
- Access and permission boundaries, documented
- Monitoring and a path to update models without rebuilding the system
What this does not promise: No system is presented as fully autonomous, and no model is treated as a source of truth. Answers stay grounded, reviewable, and bounded by the permissions you set.
Products with this capability
- Eigen ConductorAI Orchestration / Platform
- Automated Document ProcessingDocument AI / Operations
- Internal AI AssistantAI / R&D
Engineering behind it
Questions buyers ask
Is this just a chatbot?
No. The interface may be a chat window, a search surface, or a feature inside another tool. What matters is the system behind it: retrieval from your material, evaluation, permissions, review, and a record of what was asked and answered. A model is one component, and it is replaceable.
Do you train models on our data?
Training is not a default. Providers are used behind an adapter layer, data handling terms are reviewed before any model sees production material, and where policy or confidentiality requires it, the architecture can run against a model in your own environment.
How do we know the answers are any good?
Before rollout, the system is evaluated on a set of real questions with answers your team already trusts. After rollout, low-confidence cases, user corrections, and unanswered questions are visible, so quality is measured rather than assumed.
Can it work with our permission model?
Yes. Retrieval is filtered by the access of the person asking, so a system never becomes a way to see documents someone could not otherwise open. Permission boundaries are part of the architecture, not a setting added later.