Enterprise AI & RAG
Search and question-answering over approved knowledge sources, with retrieval, source references and access boundaries.
Your knowledge.Put to work.
Make documents easier to use and repetitive work easier to manage. We build AI systems around your data, permissions and the decisions that still need a person.
Plan your projectFrom your workflow to a working system
- 1Your knowledge
- 2AI + review
- 3Useful action
The right scope.
Built around your needs.
Start with the problem that matters. Combine the capabilities your project needs, with boundaries agreed before the build.
AI agents & workflow automation
Connect model reasoning to defined tools and actions, with approval steps where mistakes carry consequences.
Document intelligence
Extract, classify and organize information from documents, with validation and exception handling.
Machine learning & analytics
Explore prediction, natural language processing and computer vision where data quality and evaluation support the use case.
From evidence to a reviewed action.
A reference document-assisted workflow. Source access, answer generation and action approval remain separate decisions.
Sources: its responsibility
Retrieve relevant material from approved sources within the requesting user’s permissions.
What to review
Check document freshness, deletion, source ownership and permission enforcement.
Illustrative planning model. Your architecture and deliverables are agreed during scoping.
See where
it could take you.
Illustrative applications to help shape your brief. These are possible project scopes, not customer case studies.
The starting point
Important answers are scattered across policies, manuals and internal documents.
The approach
Index approved sources, retrieve relevant passages and return answers with references while respecting access rules.
What the work can produce
A knowledge assistant prototype, ingestion pipeline and an evaluation set built around real questions.
Start where
your project is.
The first step depends on what is already known. These starting points help define an engagement; scope, price and timing are agreed separately.
Explore an uncertain idea
The problem is clear, but the approach is not.
- 1Question
- 2Prototype
- 3Decision
What you take forward
A bounded experiment, findings and a recommendation on what to validate next.
A prototype is not a production-ready service.
Deliver a defined scope
You know the workflow and can agree acceptance checks.
- 1Scope
- 2Build & review
- 3Handover
What you take forward
Agreed deliverables, verification evidence and operating instructions for the scoped release.
Changes to scope need an explicit decision on cost and timing.
Improve an existing system
A live system needs focused changes or ongoing care.
- 1Review
- 2Prioritize
- 3Release & observe
What you take forward
A prioritized backlog, reviewed changes and an agreed maintenance or support boundary.
Coverage, response targets and access are agreed per engagement.
A clear path.
Through the work.
Scope and deliverables are agreed for each project. These stages guide the conversation.
- 1
Choose a measurable task
Understand the current workflow, available data and how the team will judge a useful result.
- 2
Evaluate before expanding
Prototype with representative inputs, inspect failures and compare results against an agreed baseline.
- 3
Integrate with oversight
Connect the workflow, define review boundaries and prepare monitoring, feedback and handover.
Model choice follows the evidence and the task
Technology is a scoping decision. These are relevant tools and building blocks to discuss against your existing systems, operating constraints and maintenance needs.
Sources
Document parsingRetrievalPermissions
Choose source formats and retrieval behavior around the approved material and each user’s access.
Application
Model APIsNode.jsTypeScript
Compare candidate model behavior on representative tasks before committing to a provider or deployment arrangement.
Operations
Evaluation setsLogsReview gates
Define an evaluation process and monitor answer quality, latency and operating cost within the agreed privacy boundaries.
The ecosystem.
Behind the experience.
Interfaces, application logic, data and operations. The building blocks work together around your project.
Frontend Development
- React
- Angular
- JavaScript
- HTML5
- CSS3
Backend Development
- Node.js
- Express.js
- Laravel
- PHP
Database
- MySQL
- PostgreSQL
- MongoDB
- Firebase
Cloud & DevOps
- Azure
- AWS
- Google Cloud
- Docker
- Jenkins
What should be checked
before handover.
Turn quality expectations into reviewable evidence. The exact checks and responsibilities belong in the project scope.
Grounded output
Evaluate answers against expected results and check that cited passages actually support the answer.
Safe boundaries
Test restricted documents, missing evidence and unsupported tasks; keep consequential actions behind agreed approvals.
Repeatable evaluation
Record evaluation cases and limitations so changes to models, prompts or source data can be reviewed before release.
Before
we begin.
Practical answers to help turn your idea into a scope everyone understands.
Will our data train a public model?
Data handling depends on the selected model provider and deployment. Retention, training use, access controls and hosting requirements must be reviewed and agreed before using your data.
Can AI run without human review?
Some low-risk tasks can be automated. Decisions with operational, financial or other significant consequences need boundaries and appropriate review. The design should account for incorrect or incomplete outputs.
Do we need a large dataset to begin?
Not always. A document assistant may start with a focused set of reliable sources. Custom machine learning may require more representative data; discovery establishes what is feasible.
Often better together.
Before you scope the project.
Still have questions?
Start with the brief.
Bring the problem you want solved. We’ll shape it into a project brief you can keep — or let AI draft a first approach.