Unstructured document analysis
Turn contracts, RFPs and reports into structured insights by extracting relevant clauses, entities and actionable information.
Data & AI Solutions | Generative & Agentic AI
Move beyond simple search. Datacation equips your teams with AI that turns mountains of documents into clear answers, and with agents that carry out tasks across your systems.
What is it?
Generative AI can understand and create text and other content. Grounded in your organisation's information, it helps people find knowledge, analyse documents and draft communications. Agentic AI goes a step further: agents work towards a goal, gather information and take permitted actions in connected tools.
Generative AI can help your team find, assess and create information. An agent can also take defined actions in connected tools. Together, we decide which tasks benefit from that step, what the agent may access and when a person must review or approve its work.
What we offer
Turn contracts, RFPs and reports into structured insights by extracting relevant clauses, entities and actionable information.
Help people find answers across company knowledge, with references back to the underlying material so they can inspect the source and apply their own judgement.
Convert raw information into useful text. Summarise transcribed meetings, identify action items and prepare drafts of complex reports for review.
For a defined task, give an agent the ability to gather information and take permitted actions in connected tools. Your team sets the permissions, checks and moments for human approval.
Agentic maturity
We see organisations move through four phases. In each one, AI takes on more of the work, and your role shifts from doing the work to directing it.
You ask a question and get an answer. Acting on it, in your systems and processes, is still up to you.
You ask the agent to carry out a task, such as creating a new product category in your ERP system.
You set the outcome and outline the approach. Agents work like junior colleagues and coordinate with each other.
You only set the goal. Agents work like senior colleagues and decide for themselves how to reach it.
An agent needs more than access to documents. Your team also knows how exceptions are handled, who may approve an action and when to ask for help. Datacation works with your people to make that context explicit and test where an agent should stop and hand over. Explore Datacation’s AI Hub
Case study
“The calm (read: less stress) our employees have gained from this is crucial…”
THB Verhoef supplies engine parts to hundreds of maritime customers. Its sales team sorted and assigned every order and quote request email by hand, which slowed the path to invoice.
A workflow that labels each incoming email by message, customer and engine type, built on THB's domain knowledge, and routes it to the right person.
Emails are now classified automatically, so the sales team no longer sorts them. Engine type is recognised with 98.5% accuracy.
NLP text classification, business rules, engine-type recognition.
What’s in it for you
We design AI around the work that needs doing: from finding an answer or drafting a document to completing defined tasks across your existing systems.
We connect answers and agent actions to relevant organisational information, provide sources where appropriate, and build in permissions, checks and human review for consequential decisions.
Reduce repetitive reading, sorting and drafting so your people can focus on exceptions, relationships and decisions that need their experience.
The expertise behind it
Ziad is an LLM consultant who builds assistants and agents on top of an organisation's own documents and systems. He helps teams choose a first task, ground answers in sources people can check, and set the permissions and human review an agent needs before it acts.
Frequently asked
We choose deployment and provider settings with your privacy and security requirements in mind. Depending on the solution, that may mean a private deployment or a commercial API with contractual and technical controls on data use. We agree those choices with you before connecting your documents.
Grounding answers in your own material, showing sources and testing against real questions can reduce unsupported claims, but it cannot eliminate them entirely. For higher-impact answers and actions, we add appropriate checks and human review.
That depends on the task and its risks. An agent might only prepare an action for approval, or it might complete a narrowly defined task within agreed permissions. We define its boundaries, escalation points and oversight before allowing it to act.
Start with one task your team knows well. In an AI Quick Scan, we examine the information it needs, the systems involved and the risks of getting it wrong. Then we decide together whether an assistant, an agent or a simpler workflow is the right first step.
It does not have to be perfect. We first assess which information is usable and where important gaps exist. For more autonomous workflows, written policies alone may not be enough: the agent may also need context about practical decisions and unwritten rules that experienced colleagues rely on.
Tell us what you are working on. We will get back to you and explore together what data and AI can do for you.
