Quantarq Intelligence
Applied intelligence for the enterprise.
01What we build
Practical AI, engineered for real operations.
Each capability is designed around a business workflow, so it can be measured, trusted, and maintained.
- 01
Enterprise AI integration
We connect modern AI models to your systems, data, and access rules, so intelligence works inside the tools your teams already use.
- 02
Intelligent document processing
Contracts, invoices, tenders, and forms are read, classified, and validated automatically, with human review where judgement matters.
- 03
Knowledge management and retrieval
Policies, procedures, and project history become searchable in plain language, with answers that cite the documents they came from.
- 04
AI-powered business workflows
Intelligent steps inside approvals, intake, and routing let routine work move forward without manual handoffs.
- 05
Advanced analytics
Operational data is modelled into forecasts, risk signals, and decision support that leadership and teams can act on.
- 06
Intelligent assistants
Purpose-built assistants for operations, finance, and project teams that understand your terminology and your processes.
- 07
Process automation
Repetitive, rules-based tasks are automated across systems, with monitoring and clear exception handling.
- 08
Private and secure AI
Where the technology and your requirements allow, deployments are designed to keep sensitive data within your own governance boundaries.
02Connected intelligence
From scattered information to decisions.
Intelligent services sit between the sources your organization already has and the outcomes your teams need.
Sources
- DocumentsContracts, invoices, specifications
- Business systemsERP, finance, HR, operations
- Operational dataRecords, transactions, metrics
- People and requestsQuestions, approvals, tasks
Quantarq Intelligence
01Knowledge and retrieval
Indexes documents and records with their permissions
02Models and reasoning
Reads, classifies, summarizes, and answers
03Workflow automation
Acts inside approvals, intake, and routing
04Governance and review
Human sign-off where judgement matters
Outcomes
- Answers with sourcesGrounded in your own knowledge
- Structured dataExtracted, validated, ready to post
- Automated stepsRouting, reminders, updates
- Signals and alertsExceptions raised early
03How it fits together
An architecture you can govern.
Enterprise AI is a system, not a single model. We design each layer so it can be understood, secured, and improved.
- L6People and decisionsReview, approval, and accountability stay with the people responsible.
- Human review
- Approval routing
- Feedback
- L5ApplicationsIntelligence delivered where work happens.
- Assistants
- Document workflows
- Analytics
- Automation
- L4Models and reasoningThe right technique for each task, evaluated against your requirements.
- Language models
- Extraction
- Classification
- Forecasting
- L3Knowledge and retrievalYour documents and records organized so answers can cite their sources.
- Indexing
- Search
- Permission-aware retrieval
- L2Integration and governanceControlled connections to the systems you already run.
- Connectors
- Access control
- Logging
- Data policies
- L1Your data and systemsThe foundation everything else is grounded in.
- Documents
- ERP and finance
- HR
- Operations
04Principles
How we approach enterprise AI.
- 01
Grounded in your data
Answers come from your own documents and records, and show where they came from.
- 02
People in the loop
Intelligence proposes and prepares. People approve wherever judgement or accountability matters.
- 03
Respectful of permissions
Systems see only what the people using them are allowed to see, using the access rules you already have.
- 04
Measured against outcomes
We agree what success looks like before we build, then evaluate the system against it.
- 05
Private where required
Where the technology and your requirements allow, sensitive workloads run inside your own environment.
05Where it applies
Examples of intelligence at work.
Illustrative applications of the capabilities above. They describe what can be built, not customer case studies.
- Example
Tender and contract analysis
Summaries of requirements, obligations, and risks across long tender packages and contracts.
- Example
Invoice and document intake
Fields read and validated from invoices, quotations, and delivery notes before anyone re-types them.
- Example
Policy and procedure assistant
Plain-language answers from HR, procurement, and operations policies, with the source passage attached.
- Example
Operational reporting
Narrative summaries and exception alerts drawn from operational and financial data.
06Engagement model
Start focused. Prove value. Then scale.
- 1
Discover
Identify the workflows where intelligence creates measurable value, and the data they depend on.
- 2
Prototype
Build a focused working version against real documents and evaluate it with your team.
- 3
Integrate
Connect it to your systems, permissions, and approval paths, with monitoring in place.
- 4
Operate
Measure performance, refine the system, and extend it to the next workflow.
Put intelligence to work.
Tell us about the documents, knowledge, or decisions that slow your teams down. We will help you find where AI can make a measurable difference.