Internal copilots
Assistants grounded in your own documentation, policies, contracts and system data - answering the questions that currently require finding the one person who knows.
AI & Automation
AI that works inside the business.
Start with the business problem, not the model. AI becomes valuable when it has access to real company data, sits inside a real workflow, and produces an outcome someone is willing to act on.
ProjectData builds production AI applications for companies: agents and internal copilots connected to company systems, document and knowledge workflows, sales and customer-operations automation, and decision support - with the integration, evaluation and oversight that production use requires.
Mental model
The model is the last decision, not the first.
Every AI project we take on is designed in this order. Reversing it is the most common reason pilots never reach production.
- Business problem
- Workflow
- Data
- AI
- Operational outcome
A model that summarises documents is a feature. A system that receives supplier invoices, extracts what matters, checks it against the order in the ERP, routes the exceptions to a human and posts the rest is an operational outcome. The difference is not the model.
Most of the engineering in a useful AI system is not the AI. It is the integration, the permissions, the error handling, the evaluation and the interface where a person accepts or rejects the result.
What we build
Applications, not experiments.
Each of these is a system with inputs, owners, failure modes and a measurable effect on how work gets done.
AI agents & intelligent workflows
Multi-step processes where the system gathers information, takes defined actions in connected systems, and escalates what it should not decide alone.
Document processing
Invoices, contracts, orders, claims and forms turned into structured data, validated against source systems, with exceptions routed to people.
Knowledge systems
Retrieval over internal knowledge that respects existing permissions and cites its sources, so answers can be verified rather than trusted blindly.
Sales & customer operations
Preparation, qualification, follow-up drafting, account summaries and response handling - inside the CRM and support tools your teams already use.
Decision support
Systems that assemble the relevant evidence, present a recommendation with its reasoning, and leave the decision with the person accountable for it.
Process automation
The repetitive work between systems: re-keying, checking, chasing, reconciling and moving data between tools that do not talk to each other.
AI evaluation systems
The test harnesses, evaluation sets and monitoring that tell you whether an AI system is getting better or worse - and whether it is safe to ship a change.
Production
From proof of concept to production.
A demo proves the idea is possible. None of the following is optional once real users depend on it.
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Integration
Connected to the systems that hold the truth - ERP, CRM, document stores, internal applications - rather than to an extract that was current the day the demo was built.
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Data quality
Known gaps, duplicates and stale records handled explicitly. An AI system inherits every weakness of the data it reads, and presents it confidently.
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Permissions
The application respects the access model that already exists. A user should not learn something through an assistant that they could not open directly.
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Evaluation
A representative set of real cases with known-good outcomes, run on every meaningful change, so quality is measured rather than sensed.
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Reliability
Defined behaviour when a model is slow, unavailable, or returns something unusable - including a path that does not silently produce a wrong answer.
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Human oversight
A designed review step wherever the cost of being wrong is real, with the reviewer given enough context to judge quickly.
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Monitoring
Visibility into quality, latency, cost per outcome and usage - and an owner who looks at it after the project team has moved on.
What we will tell you if AI is the wrong answer.
Plainly, and early.
The process needs certainty
If the output must be exactly right every time and the rules are knowable, deterministic software is cheaper, faster and easier to defend.
The data is not there yet
If the information the model would need is fragmented or unreliable, the honest first project is a data project.
The volume does not justify it
Automating something that happens twice a month rarely repays the cost of building, running and maintaining it.
FAQ
Common questions about AI in the enterprise.
What business processes can AI realistically automate?
The processes where AI performs best share a shape: high volume, language- or document-heavy, rule-based but not rigid, and currently done by people reading and re-keying. Document intake and classification, extracting structured data from unstructured inputs, drafting routine responses, summarising long histories before a human decision, triaging requests, and answering internal questions that currently require finding the right colleague.
Processes that need a guaranteed exact answer - a payment amount, a regulatory filing - are usually better served by deterministic software, sometimes with AI handling only the messy input at the edge.
How do you move an AI proof of concept into production?
A demo has to work once, on chosen inputs, for a friendly audience. Production has to work every day, on real inputs, for people who will be held accountable for the result.
The gap is made of specific engineering work: connecting to real systems rather than an exported sample; respecting existing permissions so users see only what they are entitled to see; an evaluation set that tells you whether a change made things better or worse; handling of failures and ambiguous cases; monitoring of quality, cost and latency; and a designed path for human review where the decision matters.
Does an AI application need access to company data?
To be useful inside a business, almost always yes. A model with no access to your data can only produce generic output. The useful version answers questions about your customers, your orders, your policies and your history.
That access should be deliberate and scoped: specific systems, specific data, honouring the permission model that already exists, with a record of what was accessed. Deciding what an AI system is allowed to see is a design decision that belongs to the business, not an implementation detail.
Where should a company start with AI?
With a process that is expensive, repetitive and measurable - not with the most visible one. Choose something where you can state today's cost in hours or headcount, where the output can be checked, and where a partial improvement is still worth having.
Start narrow enough that it can be in real use within weeks. A working system in one process teaches an organisation more about its own readiness than a broad strategy exercise.
What if our data is not ready for AI?
That is common, and it is not always a blocker. Some applications - document processing, drafting, summarisation - need little structured data. Others, such as recommendations or decision support, depend directly on data that is joined and trustworthy.
Where the data layer is the real constraint, we say so, and the honest first project is usually a data project rather than an AI one.
Will people actually use it?
Only if it sits inside the work they already do. AI delivered as a separate tool that people must remember to open is usually abandoned within a month.
We put it where the work happens - the CRM, the internal application, the ticketing system, the inbox - and design the interaction so the human keeps control of the decision.
Which repetitive process costs you the most?
Describe the work your team does over and over. We'll tell you whether AI, automation or ordinary software is the right way to remove it.