Services

AI engineering and software development in one place

Most projects need both. Grouped below by the kind of work involved, so you can find the starting point that matches your situation.

AI Engineering

Applied AI work, from the first feasibility question through to a monitored feature running in production.

Generative AI

Drafting, summarising, extraction, and classification features built into your product, with validation around the output.
RAG and knowledge systems

Answers grounded in your own documents, with citations, permission-aware retrieval, and a measured retrieval test set.
AI agents and automation

Multi-step processes that act through your systems, with scoped credentials, approval gates, and a full audit trail.
Machine learning

Forecasting, scoring, and anomaly detection built on your history — beginning with whether the data supports it.
Document intelligence

Extraction from PDFs, scans, and attachments, with confidence scores, validation rules, and a human review queue.
Predictive analytics

Models deployed into an existing workflow, with drift monitoring and a defined retraining process.

Software Engineering

Full-stack development for the systems your business runs on, whether or not AI is part of them.

Web applications

Browser-based applications built around your process, with role-based access and a documented data model.
Business systems

Internal systems for operations, approvals, scheduling, and reporting, replacing spreadsheets and email threads.
SaaS platforms

Multi-tenant products with accounts, roles, billing integration, and the admin tooling your support team needs.
APIs

Documented, versioned REST APIs holding your business rules in one place instead of in each client application.
Cloud systems

Containerised builds, CI/CD pipelines, separate environments, and monitoring that shows what production is doing.
System modernization

Staged improvement of software that still works but has become slow, fragile, or unsupported.

Technical Support

Shorter engagements for teams that need a specific answer, a second opinion, or ongoing capacity.

Architecture consulting

A review of a proposed or existing design, with the trade-offs and risks written down in language your stakeholders can act on.
Proof of concept development

A time-boxed build that answers one question: is this feasible, at what quality, and at what cost per request.
Performance improvement

Profiling and targeted work on slow pages, slow queries, and slow model calls, reported with before and after measurements.
Security improvement

Review of authentication, authorisation, data handling, dependencies, and headers, with prioritised remediation work.
Testing

Adding automated test coverage around the areas that change most often, so releases stop depending on manual checks.
Maintenance

An agreed monthly scope covering updates, monitoring, defect fixes, and a steady stream of small improvements.

Choosing an engagement

Which one fits your situation?

Four common starting points. If you are between two of them, the shorter one is usually the safer first step.

AI proof of concept

Best when: You need to know whether an AI approach is feasible on your data before committing to a build.
Typical scope: One clearly stated question and one dataset
Data assessment and a baseline to compare against
A working prototype, not a production system
Measured accuracy, cost per request, and latency
What you get: A prototype plus a written recommendation: proceed, adjust the approach, or solve it without AI.

Shortest engagement. Time-boxed and scoped to a single question.

Production AI system

Best when: The approach is proven and the feature now needs to be reliable, monitored, and cost-controlled.
Typical scope: Data pipeline, retrieval or model serving, and application integration
Evaluation set and regression checks in the release process
Permissions, audit logging, and human review where needed
Cost, latency, and quality monitoring with alerts
What you get: A deployed feature with measurements, documentation, and an owner for each moving part.

Substantial. Roughly comparable to a custom software project of similar scope.

Custom software project

Best when: A process needs a system built around it, with or without any AI component.
Typical scope: Discovery of the process as it actually works today
Data model, application, and integrations
Automated tests, deployment pipeline, and environments
Handover documentation and team walkthrough
What you get: A working system in production, with the source code and documentation delivered to you.

Scales with process complexity and the number of systems it must connect to.

Existing system improvement

Best when: Software already runs the business but has become slow, fragile, or expensive to change.
Typical scope: Assessment with prioritised technical risks
Staged work that keeps the system available throughout
Test coverage added around the areas being changed
Dependency, runtime, and infrastructure upgrades
What you get: Measurable improvement against agreed targets, and a system your team can safely change again.

Flexible. Often runs alongside a maintenance agreement.

Still unsure? Describe the problem rather than the solution. Part of our job is working out which of these is the right route — including the case where a smaller change solves it and no project is needed.

Tell us what you are trying to solve

Send a short description of the process, product, or system you have in mind. We will reply with a practical technical direction and the questions we would need answered first.

Discuss Your Project See example solutions

Or email contact@solvexasystems.com