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AIoverflow.tech
Services

From AI capability to a working business system.

We connect AI, documents, data and daily operations. From workflow discovery and product design to integrations, evaluation, deployment and ongoing improvement. Individual, startup, or established business — we start by working out whether AI is the right tool at all, then build and operate the systems that get the work done.

Book an AI feasibility audit
01What we do

Capabilities across the whole workflow.

01

Discovery & solution design

Turn an operational problem into a buildable plan.

We map the people, data, systems and exceptions behind a workflow. We compare AI, conventional software and simpler process changes, then define the architecture, acceptance criteria and first useful release.

Example engagement

Map an accounts-payable workflow from incoming documents to approved ERP entries, including the exceptions that need a person.

See an example workflow

Typical deliverables

  • Workflow and data map
  • Prioritised scope and architecture
  • Acceptance criteria and evaluation plan
Workflow discoveryTechnical feasibilityProduct requirements
02

AI agents & workflow automation

Connect useful AI actions to the work your team already does.

We build assistants and multi-step workflows around approved tools, clear permissions and recoverable state. They can classify requests, prepare records, draft responses and coordinate tasks, with human decisions at consequential steps.

Example engagement

Turn an incoming customer request into a grounded draft, an approval task and a verified update to the business system.

See an example workflow

Typical deliverables

  • Connected workflow and tool integrations
  • Approval, retry and exception paths
  • Execution history and operating guide
Inbox and task triageTool-using assistantsApproval workflows
03

Document AI & e-invoice workflows

Move from documents to structured, reviewable business data.

We combine OCR, language and vision models with deterministic checks for classification, splitting, multilingual extraction, complex tables and master-data matching. Document intake, generated PDFs, JSON/XML exchange and export validation belong in the same workflow.

Example engagement

Extract invoice line items, match supplier records, flag inconsistent totals and prepare an export for an accounts-payable reviewer.

See an example workflow

Typical deliverables

  • Intake, extraction and review pipeline
  • Field-level evaluation against reference data
  • Validated exports and exception handling
OCR and complex tablesVendor matchingInvoice data validation
04

Knowledge & research automation

Make information easier to retrieve, verify and reuse.

We connect documents and structured sources to retrieval, semantic search and cited research workflows. Source records, duplicate handling and review stages help teams inspect an answer before using it in a report, brief or decision.

Example engagement

Prepare an evidence-linked research brief from approved data sources, keeping conflicting facts visible for an analyst to resolve.

See an example workflow

Typical deliverables

  • Source ingestion and retrieval design
  • Grounded drafting and review flow
  • Source traceability and freshness checks
RAG and semantic searchCited research briefsDocument generation
05

APIs, integrations & data workflows

Make your existing systems work together reliably.

We build REST and GraphQL integrations, webhooks, ingestion pipelines and data mappings. Our engineering experience includes ERP export workflows, email intake, vendor-master data, queue-based processing and the reconciliation needed when a downstream system rejects a record.

Example engagement

Connect an intake application to an ERP, validate identifiers and attachments, and give operators a safe way to review and retry rejected exports.

See an example workflow

Typical deliverables

  • API contracts and field mappings
  • Idempotency, reconciliation and retries
  • Integration tests and support runbook
ERP and CRM integrationData enrichmentEvent-driven processing
06

Business operations software

Bring bookings, sales, people and finance into one working system.

We build custom operating software around the way a business actually runs: bookings, CRM, point of sale, inventory, staff attendance, commissions, payroll workflows and management reporting. Permissions and financial rules are part of the product design.

Example engagement

Connect a service business's public booking journey with its staff schedule, billing, stock, commission calculations and monthly operating reports.

Typical deliverables

  • Connected customer and back-office workflows
  • Role-based dashboards and reporting
  • Data migration, training and handover
Booking, CRM and POSWorkforce workflowsOperational reporting
07

Web, SaaS & AI product engineering

Build the whole product around the AI capability.

We design and implement web interfaces, backend services, data models, authentication, asynchronous jobs and document exports. Work ranges from a focused prototype to an operating application, including accessible websites, admin tools and browser-based companions.

Example engagement

Build a document-generation workspace with source facts, editable drafts, version history, background processing and reliable PDF export.

See an example workflow

Typical deliverables

  • Product interface and backend
  • Tested user journeys and recovery paths
  • Deployment, documentation and ownership handover
Full-stack applicationsCustomer portalsWebsites and admin tools
08

AI evaluation & quality engineering

Know what works before you change the model or release the code.

We design ground-truth datasets, field-aware scoring, consistency checks and cost/latency comparisons for LLM, vision and document workflows. API contracts, regression suites and realistic edge cases connect model quality to software release decisions.

Example engagement

Compare model providers on the same reviewed documents, inspect failures by field and gate a rollout on quality, cost and response-time criteria.

See an example workflow

Typical deliverables

  • Evaluation dataset and scoring rules
  • Reproducible benchmark and regression tooling
  • Release recommendation with measured limitations
Model benchmarksAPI regressionRelease quality gates
09

Private AI & on-premises models

Evaluate SLMs and LLMs for deployment in environments you control.

We scope and build private-deployment pilots for small language models (SLMs) and large language models (LLMs), comparing on-premises, private-cloud and hybrid options. Model selection, inference sizing, retrieval, data flows and network access are tested against the task. Local hosting is paired with access controls, logging and evaluation.

Example engagement

Pilot an internal document assistant using an on-premises SLM or LLM, with approved sources, permission-aware retrieval and measured quality, latency and hardware requirements.

See an example workflow

Typical deliverables

  • Deployment architecture and data-flow map
  • Model benchmark, hardware sizing and cost assessment
  • Scoped pilot, access controls and acceptance checks
Small language models (SLMs)On-premises LLMsPrivate deployment pilots
10

Cloud, reliability & migrations

Find the failure, make the change and keep the system observable.

We trace problems across APIs, workers, queues and databases, then prepare bounded fixes, verification and rollback. Our experience covers containerised services, multi-region releases, database migrations, backup restoration, telemetry and production support practices.

Example engagement

Diagnose a stalled ingestion pipeline, reconcile missing records in a controlled environment and release a fix with monitoring and rollback checks.

Typical deliverables

  • Evidence-based diagnosis and repair plan
  • Regression, migration and recovery checks
  • Monitoring, handover and agreed support scope
Root-cause analysisMigration and recoveryObservability and runbooks
11

Vision & interactive prototypes

Make visual workflows tangible before committing to a full product.

We prototype image-assisted interfaces, interactive 3D configuration, inspection workflows and visual decision tools. We test the user journey and technical feasibility, with explicit review of model errors, asset rights and the limits of the simulation.

Example engagement

Prototype a photo-to-mask customisation flow or a 3D product configurator that collects a reviewable design brief for a sales team.

Typical deliverables

  • Interactive proof of concept
  • Feasibility and failure-case review
  • Requirements for a production implementation
Image-assisted interfaces3D configurationInspection decision tools
12

Technical training & team enablement

Help the people using and maintaining the system become confident with it.

We combine technical teaching experience with implementation workshops, practical AI demonstrations and clear operating documentation. Training can cover programming, applied AI, workflow design, model evaluation and the specific tools delivered to your team.

Example engagement

Run a workshop around one of your team's workflows, then leave behind examples, exercises, a user guide and an escalation path.

Typical deliverables

  • Role-specific learning sessions
  • Exercises and reusable examples
  • User guides and maintenance documentation
Practical AI workshopsUser onboardingDeveloper handover
13

Search, ranking & decision support

Turn operational data into useful search, comparisons and decisions.

We develop search and matching tools, ranking workflows, analytical dashboards and machine-learning prototypes around a defined business question. Earlier engineering experience includes keyword recommendation, supplier-performance analysis and instructor analytics; new engagements begin with a data-quality and baseline review.

Example engagement

Combine supplier records, quality signals and delivery history into an explainable comparison tool that an operations team can review.

Typical deliverables

  • Data-quality review and baseline
  • Search, ranking or analytics implementation
  • Evaluation, limitations and user guidance
Search and recommendationsOperational analyticsML feasibility and baselines
14

AI privacy, security & governance

Turn AI policies and risk decisions into practical engineering controls.

We help teams map AI systems and data flows, define permitted actions, assign human reviewers and retain useful audit trails. Work can include data minimisation, retention requirements, permission-aware retrieval and model/provider evaluation. We document data provenance and model-licensing constraints for copyright and IP review with your legal and security owners.

Example engagement

Review an internal AI assistant's data access and output workflow, then implement scoped permissions, review checkpoints and a traceable record of model changes and decisions.

See an example workflow

Typical deliverables

  • AI system inventory and data-flow assessment
  • Permission, review and audit controls
  • Evaluation evidence and governance handover
Privacy by designAI risk controlsData provenance & licensing
02Automation blueprints

Real work.
Every step made clear.

See what comes in, what AI does, and where your team decides. Explore four example workflows we can design around your business.

01Customer service & revenue operations

Turn a customer request into a resolved case.

Connect the conversation to the right account and policy. Prepare the answer, get approval where needed, and keep the customer record up to date.

Starts with

An email asking to change a delivery address

Designed result

An approved reply and an updated case record

Interactive example

Sample data · no live connections

Select a step to see the work inside.

Explore the steps1 / 4
Intake agentRequest received

Customer email

Message
Can you change the delivery address for order DEMO-104?
Intent
Change delivery address
Next action
Check order and address-change policy

The original message stays attached to the case.

People stay in control. A person approves changes to customer details.

02Local LLMs & small language models

Find the right model for one focused task.

Test cloud, local and small language models on your own examples. Choose a deployment from evidence about quality, privacy and operating requirements.

Starts with

Private documents that need to be classified

Designed result

A model selection and deployment plan backed by tests

Interactive example

Sample data · no live connections

Select a step to see the work inside.

Explore the steps1 / 4
Your team + AIoverflowTest brief prepared

Evaluation brief

Task
Classify incoming documents by department
Example input
A supplier invoice with an unclear department
Expected behaviour
Route to Finance, or ask for review when uncertain

Representative and difficult examples belong in the same test set.

People stay in control. Your team sets the quality bar and approves the model.

03On-premise AI agents

Handle sensitive work inside your environment.

An internal agent checks approved records and prepares an action. Permissions, human review and execution logs remain part of the workflow.

Starts with

An internal request for access to a project folder

Designed result

A reviewed access decision with a traceable record

Interactive example

Sample data · no live connections

Select a step to see the work inside.

Explore the steps1 / 4
Internal request queueRequest received

Internal ticket

Request
Read access to the project delivery folder
Requester
An authenticated employee
Data boundary
Request and records stay in the private environment

The internal identity is linked to the original request.

People stay in control. The agent cannot grant access without an authorised reviewer.

04Document & back-office automation

Move an invoice from the inbox to a checked record.

Extract the fields, compare them with the purchase order, and surface mismatches. Your team resolves the exception before an approved record reaches the ERP.

Starts with

An emailed invoice that does not match its purchase order

Designed result

A reviewed ERP draft with the original evidence attached

Interactive example

Sample data · no live connections

Select a step to see the work inside.

Explore the steps1 / 4
Document agentInvoice captured

Invoice DEMO-208

Source
Supplier email with PDF attachment
Extracted fields
Supplier, invoice number, total and purchase order
Original
Email and document attached to the same record

A field can be checked against the document it came from.

People stay in control. A mismatch pauses the workflow. Payment is never automatic.

What would this look like in your business?

We scope the data, tools, approval rules and evaluation criteria with you. These are illustrative blueprints, not pre-connected products.

Map my workflow
AI compliance readiness

Current requirements. Practical implementation.

We help turn applicable requirements into data controls, evaluation evidence and documented operating decisions, working with your legal, privacy and security owners.

Official sources reviewed 11 October 2026. Requirements depend on your role, use case and jurisdiction.

Law · Phased obligations

EU AI Act

We help prepare AI inventories, provider/deployer role mapping, transparency controls and evaluation evidence. Article 50 applies from 2 August 2026, with a limited transition for some existing systems. Revised high-risk dates are 2 December 2027 for Annex III and 2 August 2028 for Annex I; applicability must be checked per system.

EU transparency guidanceRevised implementation dates

Law · Personal data and decisions

GDPR & UK data protection

For AI using personal data, we support data-flow mapping, impact-assessment inputs, retention controls and safeguards around automated decisions. UK work considers the Data (Use and Access) Act 2025 changes now in force, with legal bases and individual-rights requirements reviewed by the organisation's privacy owners.

EDPB impact-assessment guidanceICO: current UK changes

Rights · Provenance and permissions

Copyright, IP & model licensing

We document source provenance, model licences, permitted data use and output-review checkpoints for legal review. The UK government published its copyright-and-AI report and impact assessment in March 2026. We track that work without treating consultation options as settled permissions to use copyrighted material.

UK copyright and AI updates

Standards and guidance · Voluntary references

ISO & NIST governance frameworks

ISO/IEC 42001, ISO/IEC 42005 and the NIST AI Risk Management Framework provide useful references for AI management, impact assessment and generative-AI risk. We use them to structure ownership, risk records, evaluations and review processes. Framework use is distinct from independent certification.

ISO AI governance and impact standardsNIST AI RMF and GenAI profile

Our scope is technical readiness and implementation support. Legal opinions and independent certification remain with the relevant specialists; private hosting alone does not establish compliance.

03How we engage

A clear scope, a tested release, an operable system.

  1. 01

    Discovery call

    We learn your workflow and where the friction is. You leave knowing whether AI is worth pursuing — honestly, even if the answer is no.

  2. 02

    Scoped build

    A focused build with agreed acceptance criteria, representative test data and user review before release.

  3. 03

    Handover & support

    Deployment, operating documentation and team training, with maintenance and improvement responsibilities agreed up front.

04Engineering and delivery standards

Quality you can inspect.

  • 01Evaluate against reviewed examples, with clear scoring rules and acceptance criteria.
  • 02Use scoped permissions, explicit approvals and an inspectable action history.
  • 03Design for failed requests, retries, duplicate events and recovery.
  • 04Measure model quality, response time and operating cost together.
  • 05Validate changes with regression checks, a staged rollout and a rollback plan.
  • 06Document data handling, ownership, operating limits and support responsibilities.
  • 07Map sensitive data flows, minimise collection and agree retention and deletion requirements.
  • 08Record data provenance and model licensing constraints for copyright and IP review with the relevant owners.

Not sure if AI fits your problem?

That's the right place to start. Tell us what you're working on — we'll tell you straight.

Work with us See what we've built