AI product engineering
We build products where AI does real work – agents that complete tasks, assistants grounded in your data and models trained for your domain – engineered, tested and monitored so they hold up with real users. We take you from first use case to production, with evaluations and guardrails built in.
From AI features to AI that does the work
An AI product is no longer a chatbot added to an app. It is agents that use your systems, knowledge that stays current and evaluations that prove quality before every release. The engineering around the model now matters more than the model.
of agentic AI projects will be cancelled by the end of 2027, Gartner predicts – because of escalating costs, unclear business value or inadequate risk controls.
Source: Gartner, June 2025
- BeforeA chatbot added to an appNowAgents that complete tasks inside your systems
- BeforeDemos that impress onceNowEvaluations that prove quality before every release
- BeforeOne model for everythingNowThe right model for each task, swappable as models improve
Chatbot, copilot or agent: which does your use case need?
Most products combine more than one. Start with the simplest option that does the job, then add autonomy where it pays off.
| Option | What it does | Good for | What it needs |
|---|---|---|---|
| Chatbot or assistant | Answers questions in a conversation | FAQs, internal knowledge, first-line support | Grounded content and a clear hand-over to people |
| Copilot | Drafts and suggests inside the tools people already use; a person decides | Writing, analysis, coding and case handling | Integration with the user’s tools and the right context |
| AI agent | Plans and completes multi-step tasks using your systems | Repetitive processes with clear goals and checks | Scoped permissions, guardrails, evaluations and approval for risky steps |
| Custom or fine-tuned model | A model trained or tuned for your domain | High-volume, narrow tasks where general models fall short | Good training data, evaluation and MLOps |
What this service covers
Each capability can be used on its own or combined with capabilities from other services. “Also part of” shows the other services that include it.
AI agents & workflow orchestration
Agents that plan steps, use your systems through tools and ask people for approval – one agent or several working together.
Retrieval & knowledge (RAG)
Answers grounded in your documents and data, with sources shown and user permissions respected.
More on RAG developmentEvaluation & guardrails
Test sets, automated evaluations and red-teaming before every release, plus guardrails against prompt injection and data leaks.
Agent-ready APIs & MCP servers
Let AI assistants and agents use your product’s data and actions through documented APIs and Model Context Protocol (MCP) servers, with scoped access.
AI-assisted engineering
Coding agents in the delivery pipeline, with human review, tests and security scans on every change.
Custom models: ML, vision & small language models
Prediction, image analysis and small language models trained for your domain – as in AgriAI’s crop-health model and MealsPad’s meal planner.
Combined with capabilities from other services
This service draws on capabilities owned by other teams. Each is explained in full on its own service page.
Specialist services within AI product engineering
Each builds on the capabilities above and adds its own.
AI-native custom software development
Web, mobile, desktop and blockchain applications.
- Web apps
- Mobile apps
- Desktop apps
- Blockchain application development
Legacy modernization & AI infusion
Code discovery, step-by-step migration and AI infusion.
- AI-assisted code & system discovery
- Incremental migration
- API-first wrapping & AI infusion
- Project rescue
Includes project rescue
Explore legacy modernization & AI infusionHow we deliver it
- 01
Discovery
Product goals, users, data and the business case.
- 02
Proof of concept
Usually 2–4 weeks to check accuracy and value on your data.
- 03
MVP
A first production release, usually in 8–16 weeks.
- 04
Run and improve
Evaluations, guardrails and monitoring after launch.
- A proof of concept tested on your own data
- A production release with evaluations and guardrails
- Monitoring of quality, cost and agent actions after launch
- Source code, prompts and evaluation sets you own
- Start with one capabilityA fixed-scope piece of work, such as an assessment, a prototype or a proof of concept.
- Combine capabilities into one projectCapabilities from several services, delivered by one team with one plan and one point of contact.
- Add a dedicated teamEngineers who join your team to build and run it with you.
Selected client work
“The platform was successfully implemented, thanks to Assystant’s excellent development work. The team worked in a timely and effective manner and delivered items on time.”
Related guides
What is agentic AI? Definition, use cases and how to start
AI systems that plan steps, use tools and act with limited supervision – and how to adopt them safely.
Read the articleRAG vs fine-tuning: how to ground LLMs in your company data
Compare cost, accuracy, freshness and security – and learn when to combine both.
Read the articleWhat does it cost to build your own GPT solution?
Approach, data, integration, usage fees, security and maintenance – and how to keep cost under control.
Read the articleHow to scope an AI proof of concept that reaches production
Business outcome, use case, data checks, success criteria and a go/no-go decision.
Read the articleFrequently asked questions
What is AI product engineering?
AI product engineering is the design, build and operation of software products where AI is a core capability, not an add-on. It combines product strategy, data, models, agents, user experience and production engineering, so the AI works reliably for real users.
How long does it take to build an AI product or MVP?
Most AI MVPs take 8–16 weeks from discovery to a first production release, depending on data readiness and integrations. We usually start with a 2–4 week proof of concept to check accuracy and value on your data before scaling.
Do you build AI agents and multi-agent systems?
Yes. We build agents that plan steps, use tools through APIs and Model Context Protocol (MCP) servers, and ask people for approval where it matters. When several agents work together, we design how they divide the work and log every action.
How do you know the AI is good enough to launch?
We agree quality measures with you, build a test set from real cases and run automated evaluations on every change. We launch when the results meet the bar, and keep monitoring quality after release.
Which AI models do you use?
We choose for each task – leading commercial models, open-weight models or small models trained for your domain, like the one we built for MealsPad – and design the product so a model can be swapped when a better or more efficient one appears.