Recover business rules
We trace rules buried in scripts, forms and composition logic, and have business owners confirm them before anything is rebuilt.
AI engineering, integration and validation
AI Engineering, Integration and Validation Services
We engineer AI into enterprise applications, customer communication estates and regulated processes — integrated with your systems, kept under human review, and validated where it has to be. The work is the integration, workflow and compliance around the model.
AI engineering
What we do
Four services, each starting from an operating problem and adding AI where it creates leverage.
AI inside a customer communication estate
Recover business rules, rationalise templates, assist drafting inside locked layouts and prepare accessibility remediation across large communication estates.
Explore this work ↗AI integrated into enterprise applications
Extract, classify and route work with confidence thresholds, human review, audit logging and integration into the systems of record.
Explore this work ↗AI that runs inside your own environment
Run open-weight models, retrieval and agent workflows inside your infrastructure, private cloud or specified region when data cannot leave the boundary.
Explore this work ↗Understanding systems nobody documented
Read legacy code, stored procedures and business logic to build the documentation, rule inventory and regression coverage needed before modernisation.
Explore this work ↗Communication estates
We cut the manual work in communication estates
We put AI on the slow, manual parts of running a large communication estate — recovering rules, rationalising templates, drafting inside locked layouts and preparing accessibility remediation, with human approval before anything ships.
Rationalise the template estate
We compare large estates to find near-duplicates, shared content and dead logic, turning an unscoped estate into a migration wave plan.
Assist drafting inside locked templates
We generate inside approved layouts while regulatory text, branding and structure stay fixed, with human approval before release.
Prepare accessibility remediation
We produce alt text, reading order and structural tagging across existing PDFs for human conformance review.
Human review remains the control. AI prepares more of the work; a person still reviews the output before anything reaches a customer.
See the Customer Communication Management practice.Enterprise integration
We build AI into your enterprise applications
Extraction or classification is one step. We build the production path around it — routing, review, audit, integration into systems of record and defined failure behaviour.
Typical applications: onboarding verification, claim documents, invoice and PO extraction, correspondence routing and support-ticket triage.
We size against the real workload. If a person already handles the volume efficiently, automation may cost more than it returns.
Support operations
We turn ticket history into operational knowledge
We classify and route incoming tickets, surface similar incidents and resolutions, and build a knowledge base from what engineers actually did. For existing managed-service clients, this upgrades the current support model.
Inside your boundary
We run AI inside your own environment
When security policy, data residency or disconnected sites rule out external inference, we build AI that runs entirely inside your boundary — models, retrieval and agents, with data and logs staying in.
Your infrastructure, private cloud or defined region Data stays inside the boundary
Retrieval over your content
We answer from your documents with source citations, preserving the access permissions the content already carries.
Open-weight models
We deploy inference inside your infrastructure, private cloud or specified region, with logs and data staying inside the boundary.
Agent workflows around your process
We connect agents to approval chains, systems of record, human sign-offs and defined failure handling.
Fit check
A clear fit check
We tell you where private models fit — extraction, classification, routing, structured summarisation — and where a hosted model would be materially stronger.
Legacy systems
We recover what undocumented systems actually do
When the specification no longer matches the system, we use AI-assisted code reading to recover how the application behaves, then have your owners confirm it before it becomes project input.
What we inspect
The system as it exists today
Application code, stored procedures, triggers, reports and conditional logic that accumulated after the original documentation stopped reflecting reality.
AI-assisted analysis + business-owner reviewBusiness rule inventory
Every decision, where it is implemented and where rules conflict
Functional documentation
What each module does in language the business can confirm
Data flow and dependencies
What reads from what and what breaks when an interface changes
Dead and duplicated logic
Unused paths and the same rule implemented in several places
Regression coverage
Tests derived from current behaviour so a rewrite can be proved equivalent
A defensible estimate
Scope based on what is in the code, not what the old specification claimed
Review before fact. Process owners confirm the recovered behaviour before it becomes project input.
Useful even without a rewrite. Rule inventories, dependencies and regression coverage make the current system easier to change safely.
Production AI
How we take AI to production
We take an AI use case to production through three gates: the decision, the system around it, and ownership after release.
Decision
Define the job and the human boundary
Define the decision
Set the business decision, baseline, expected output and next action before choosing a model.
Keep the right decisions with people
List cases the system must never decide alone and enforce that rule in the design.
System
Engineer for production conditions
Engineer the data path
Define sources, ownership, transformations, missing values, access and how production data reaches the model.
Design monitoring and fallback
Watch input quality, outputs and exceptions; use review, thresholds and deterministic fallbacks.
Operations
Own it after release
Assign ownership
Name owners for data changes, quality review, thresholds and retraining.
Re-measure the baseline
Check whether handling time, error rate or the target business decision actually improved.
FAQ
Questions enterprises ask before moving AI into real workflows
What does an AI assessment involve, and what do we get?
Typically two to four weeks. We look at the decision or process you want to improve, what it currently costs in time or error rate, where the data lives and what condition it is in, the regulatory classification, and the integration the result would need. You receive a written assessment: whether the use case is worth building, the approach that fits, what it would take, and the main risks. It is useful whether or not we do the build.
What makes an AI proof of concept ready for production?
Reliable data pipelines, measurable quality, integration with the real workflow, security controls, monitoring, fallback behaviour and defined ownership after release. A model that only works on a prepared demo dataset is still an experiment.
How should AI integrate with existing business applications?
As part of the application architecture, not a separate demo. Inputs, permissions, APIs, model outputs, human review, logging and exception handling each need defined interfaces so the capability operates safely inside the existing process.
Can an AI system be used in a GxP process?
Yes, treated as a computerised system in a regulated process from the start: intended use defined and approved, risk assessed against patient safety, product quality and data integrity, acceptance criteria agreed before build, traceability from requirement to evidence, change control over model and prompt revisions, and defined ownership and periodic review after release. We have run a computer system validation practice for nine years alongside the engineering team.
How do you keep AI output from reaching a customer unchecked?
By building the review step into the system rather than relying on a policy. Output carries a confidence signal, defined case types are never decided automatically, generation happens inside approved templates where fixed content cannot be altered, and every decision and human action is logged. In customer communication, generated content sits inside a locked template with an approval step before release.
Why choose AdventSys for AI and machine learning?
Because the hard part of an AI project is the integration, workflow and compliance around the model, and that is the work we do. We integrate AI into systems that already exist, build the workflow and review step around it, and validate it in regulated environments. The aim is to get something working inside your processes and keep it working.