AIoverflow builds applied AI and business software: agents, document workflows, integrations, operating platforms and the engineering that keeps them useful.
We work with founders, operators and technical teams to turn a real business need into usable software. That can mean one document workflow, a connected operating platform, or the engineering needed to make an existing AI system more reliable.
We connect the discovery conversation to the implementation details: the user interface, the data contract, the model evaluation, the exception queue and the operating guide. AI is one part of that system.
Team professional experience includes document extraction, multilingual OCR, complex tables, model-provider integration, evaluation platforms and customer-facing engineering. This is experience brought to AIoverflow engagements, separate from the AIoverflow projects shown in our portfolio.
Our client work connects the public customer journey with booking, billing, inventory, workforce workflows and management reporting. It brings the business rules and day-to-day exceptions into the software.
We work through state transitions, permissions, asynchronous processing, data correctness, regression testing and recovery. The handover includes how a system should be operated and what to check when it fails.
Our work combines engineering, product decisions and domain collaboration. Shared products retain their collaborators' contributions, and our portfolio identifies development-stage work separately from operating client systems.
Turn an operational problem into a buildable plan.
Connect useful AI actions to the work your team already does.
Move from documents to structured, reviewable business data.
Make information easier to retrieve, verify and reuse.
Make your existing systems work together reliably.
Bring bookings, sales, people and finance into one working system.
Build the whole product around the AI capability.
Know what works before you change the model or release the code.
Evaluate SLMs and LLMs for deployment in environments you control.
Find the failure, make the change and keep the system observable.
Make visual workflows tangible before committing to a full product.
Help the people using and maintaining the system become confident with it.
Turn operational data into useful search, comparisons and decisions.
Turn AI policies and risk decisions into practical engineering controls.
These are workflows we can help scope and build. The right deployment and level of automation depend on your data, users and review requirements.
Invoice and purchase-order extraction, supplier matching, reconciliation, e-invoice data exchange and reviewed ERP exports.
Bookings, customer records, POS, stock and batch handling, staff workflows, commissions and management reporting.
Clinical documentation prototypes, transcription and draft summaries for professional review, with deployment requirements scoped before any real patient use.
Inspection checklists, explainable scoring, audit lifecycles, report generation and controlled document access.
Source-linked briefings, retrieval, document generation, structured data connectors and editorial review workflows.
Accessible websites, portals, guided discovery, visual configurators, content operations and enquiry journeys.
04 / Privacy, security & AI governance
Private AI deployments and practical governance belong in the same conversation. We help scope on-premises SLM and LLM pilots, then connect model evaluation with data access, human oversight, audit records and operating responsibilities.
Explore current AI compliance readinessEvaluate against reviewed examples, with clear scoring rules and acceptance criteria.
Use scoped permissions, explicit approvals and an inspectable action history.
Design for failed requests, retries, duplicate events and recovery.
Measure model quality, response time and operating cost together.
Validate changes with regression checks, a staged rollout and a rollback plan.
Document data handling, ownership, operating limits and support responsibilities.
Map sensitive data flows, minimise collection and agree retention and deletion requirements.
Record data provenance and model licensing constraints for copyright and IP review with the relevant owners.
Python, TypeScript, Go, React, Next.js, FastAPI, Flask, Node.js, REST and GraphQL
PostgreSQL, MongoDB, SQLite, Redis, vector search, data validation and migrations
LLM/VLM integrations, OCR, embeddings, RAG, tool calling, streaming audio and evaluation
Containers, Kubernetes, Helm, queues, OpenTelemetry, CI/CD, API contracts and regression tests
01
We map the users, current systems, data and exceptions. The result is a practical scope with acceptance criteria and a clear owner for each decision.
02
We implement a focused workflow, test it with representative cases and review it with the people who will use it. Pilots and production releases have explicit readiness checks.
03
We prepare deployment, monitoring, recovery and handover. Training and an agreed support scope help your team operate the system and decide what to improve next.
Engagements can start with an assessment, a prototype, a focused implementation or help with an existing system. We agree deliverables, access, ownership, documentation and support responsibilities as part of the scope.