Document intelligence
Contracts, tenders, invoices, claims, applications and correspondence read, classified and extracted into structured data.
At Infynix, we craft growth engines that inspire and endure.
We build custom AI agents and workflow tools that automate daily administrative tasks. Off-the-shelf AI tools rarely connect to your internal company files or CRM software. AI tools should integrate directly into your daily software to save actual staff hours.
OUR CAPABILITIES IN
Artificial Intelligence & AI Integration
01
The Challenge
02
Our Insight
03
The Solution
04
The Outcome
05
Scale & Optimize
How We Work
01
Measure
We audit your current systems, marketing, and funnels.
02
Design
We architect a custom strategy bridging your exact gaps.
03
Build
We deploy the tech, content, and pipelines required.
04
Launch
We seamlessly roll out the solution with zero downtime.
05
Re-Measure
We track every metric to ensure it meets our strict KPIs.
06
Scale
We optimize the winning channels to compound your returns.
Artificial intelligence and AI integration for UK businesses that want the technology doing real work: reading and classifying documents, answering customers and staff from the company’s own knowledge, extracting data from tenders, contracts and invoices, forecasting demand, and taking the repetitive judgement calls that currently sit in a queue. We build these systems from our office in Dagenham for clients across the country, on the company’s real data, under UK GDPR and the ICO’s guidance on AI.
The British regulatory picture is specific. The ICO expects a lawful basis, transparency and, for significant automated decisions, human review and a route to challenge. Firms selling into Europe also fall under the EU AI Act, whose obligations depend on the use case. Sector rules apply on top: the FCA for financial firms, the SRA for solicitors, CQC for care. None of this prevents useful AI. It shapes where a model decides, where it recommends, and what is logged.
We choose the model for the job rather than the headline, run it privately in UK cloud regions where the data demands it, retrieve answers from the company’s own documents so the system cites sources instead of inventing them, evaluate accuracy against a test set before launch, and monitor cost and quality after. Where a plain rule or a small model does the job, that is what ships.
Law and accountancy firms, insurers and brokers, property businesses, manufacturers and distributors, healthcare and care providers, recruitment agencies, customer service operations, and SaaS companies adding AI features to their product.
What we build
Knowledge assistants
Staff and customer assistants that answer from the company’s policies, manuals and records, with sources shown.
Customer service AI
Triage, drafting and resolution across email, chat and WhatsApp with handover to people and full logging.
Forecasting and scoring
Demand, cash, churn and lead quality models built on the company’s history and tested honestly.
Process decisioning
Approvals, routing and checks made by model where safe, with human review where the ICO expects it.
Private deployment
Models run in UK regions or on private infrastructure when data cannot leave the business.
Evaluation and monitoring
Test sets, accuracy measures, cost tracking and drift alerts so quality is known, not assumed.
AI in your product
Features built into SaaS and customer products with the guardrails a UK or EU buyer will ask about.
Typical engagements
| Engagement | Typical duration | Output |
|---|---|---|
| AI opportunity review | 1 to 2 weeks | Use cases ranked by value, feasibility and risk, with a plan |
| Proof of value | 3 to 6 weeks | A working system on real data with measured accuracy |
| Production build | 6 to 14 weeks | Integrated, monitored, documented system with governance |
| Document processing pipeline | 4 to 10 weeks | Capture, extraction, validation, posting to systems |
| Assistant deployment | 4 to 8 weeks | Knowledge base, retrieval, interface, evaluation, handover rules |
| Managed AI | Monthly | Monitoring, retraining, cost review, model updates |
Who it is for
Law and accountancy
Document review, drafting support and onboarding checks with the audit trail a regulator expects.
Insurance and brokers
Claims and policy documents processed, queries answered, fraud signals flagged for people.
Property
Applicant handling, document checks and maintenance triage across a portfolio.
Manufacturing and distribution
Demand forecasting, supplier document processing and technical knowledge assistants.
Healthcare and care
Administrative automation and staff assistants built with the controls the sector requires.
SaaS
AI features in the product, with evaluation and cost control from the start.
Ways of working
Review and roadmap
A short engagement that identifies the use cases worth doing and the ones to avoid.
Proof of value
One use case built on real data with measured results before committing further.
Production programme
Use cases delivered in sequence, each integrated, evaluated and governed.
Managed AI
Ongoing monitoring, improvement and cost control of what is in production.
Answers with sources, from your own documents
A general model asked about a company’s cancellation policy, a product’s specification or a clause in a standard contract will produce something fluent and possibly wrong. The systems that work in business retrieve the relevant passages from the company’s own documents first, then answer from those passages and show them, so a member of staff or a customer can check.
We build the retrieval layer over the company’s policies, manuals, records and case history, keep it in step as documents change, evaluate answers against questions with known correct responses, and return a clear “I do not know” with a route to a person when the documents do not cover it.
Retrieval first
Answers grounded in the company’s own documents.
Sources shown
Every answer cites the passages it used.
Kept current
The knowledge base updates as documents change.
Honest limits
A clear handover when the material does not cover the question.
Automated decisions the ICO would recognise
UK GDPR restricts decisions made solely by automated means that have legal or similarly significant effects, and the ICO expects transparency, meaningful human involvement where it matters, and a way for people to challenge outcomes. Credit, employment, tenancy, insurance and access to services are the obvious cases. Firms selling into Europe also have the EU AI Act’s risk categories to consider.
We design each use case with a written decision on where the model decides, where it recommends and a person confirms, and where it only assists. Logging captures inputs, outputs and the human action, so the business can explain any outcome and evidence its governance.
Decision map
Where the model decides, recommends or assists, written down.
Human review
Built in where the decision is significant.
Full logging
Inputs, outputs and human actions recorded.
Explainable
Any outcome can be explained to the person affected.
Private where it needs to be, practical where it does not
Not every AI workload needs a private model. A marketing assistant drafting social posts can use a commercial API. A system reading client files at a law firm, patient correspondence at a care provider or claims at an insurer needs data to stay in the UK, with a documented processor arrangement or no third party at all.
We deploy models in AWS London, Azure UK South or on private infrastructure where confidentiality requires it, use commercial APIs with UK data handling and contractual terms where they are appropriate, and document which data goes where so the DPIA and supplier questionnaires are answered from fact.
UK regions
Models and data kept in London or UK South where required.
Private options
Open-weight models on private infrastructure for sensitive work.
APIs when suitable
Commercial models with UK data terms for lower-risk tasks.
Data flows documented
What goes where, for the DPIA.
Measured before launch, watched after
An AI system that was impressive in a demonstration and unmeasured in production is a risk. Accuracy drifts as documents, customers and language change, costs rise with usage, and nobody notices until a customer complains or the bill arrives.
Every system ships with a test set and an accuracy target agreed with the business, a cost budget with alerts, and monitoring of quality signals such as handover rates and corrections. Managed AI covers reviewing the results, retraining or re-prompting, and updating models as better ones become available.
Test set
Accuracy measured against known-correct examples before launch.
Cost budget
Usage tracked with alerts, not discovered on the invoice.
Quality signals
Handovers, corrections and feedback monitored.
Kept improving
Retraining, prompt updates and model changes managed.
Further reading
Artificial Intelligence & AI Integration in our other markets
Infynix Solutions is registered separately in India, the United Kingdom and the United Arab Emirates. This page covers United Kingdom. The same service, written for the other markets:
Artificial Intelligence & AI Integration questions
How can AI help our business?
AI can answer common customer questions, process incoming document data, and automate daily scheduling.
Where should we start with AI?
With a short review that ranks use cases by value, feasibility and risk. Document processing, knowledge assistants and customer service triage are usually first because the data exists and the return is measurable.
Will our data be used to train public models?
No. Systems are built on your data under your control, commercial APIs are used only with terms that exclude training, and sensitive workloads run privately in UK regions.
How do you handle UK GDPR and the ICO’s AI guidance?
Each use case has a documented lawful basis, a decision map showing where humans review, full logging of inputs and outputs, and a DPIA where one is needed. We build to the ICO’s guidance and the EU AI Act where you sell into Europe.
Can the assistant answer from our own documents?
Yes. Retrieval over your policies, manuals and records means answers come from your material with sources shown, and the assistant hands over to a person when the documents do not cover the question.
Which models do you use?
Whichever suits the task and the data: commercial models through APIs with UK data terms, open-weight models on private infrastructure, or small specialised models. We choose per use case and can switch as better options appear.
How do we know it is accurate?
A test set and an accuracy target are agreed before launch, results are measured against it, and quality signals are monitored in production with reviews under managed AI.
How long does it take to get something live?
A proof of value on real data takes three to six weeks; a production system with integration, monitoring and governance six to fourteen.
Can you add AI features to our SaaS product?
Yes. We build features into products with evaluation, cost control, privacy handling and the documentation UK and EU customers ask for in security reviews.
