Solutions · Product Development
Ship AI products and agents that work in the real world
AI makes impressive demos easy and reliable products hard. We help you scope the smallest product that proves value, build agents you can trust, and scale only what customers actually use.
Activo Group brings product, design, data and AI engineering together to take your idea from validated problem to production, with evaluation, cost control and human oversight built in from day one.
- Digital products
- AI agents & copilots
- Internal tools
- MVPs & pilots
Why AI products are harder than they look
Product development has shifted from building predictable software to engineering systems that plan, call tools and act on their own. Most AI demos never become products, not for lack of technology, but because they solve the wrong problem or can’t be trusted in production.
Traditional software vs agentic systems
| Traditional software | Agentic systems | |
|---|---|---|
| Behaviour | Same input, same output | Varies run to run; plans its own steps |
| Testing | Rules and unit tests | Evaluating whole multi-step trajectories |
| Experience | Screens and forms | Plans, approvals, controls and escalation |
| Cost | Mostly fixed hosting | Per task, growing with context and usage |
| Failure | Bugs you can reproduce | Drift, hallucination and tool errors |
Hard truths we design around
- AI is great at first drafts, not yet at final answers
- AI prepares; a human reviews, confirms or redirects
- Poor data means poor outcomes, so data work comes first
- Automating a chaotic process just produces chaos faster
- Not every product needs AI; it should be invisible where it helps
Build the smallest thing that proves value
With AI coding tools, building is no longer the bottleneck: knowing what to build is. Start with a minimum viable segment, one group with the same painful problem, then design a minimum viable product that solves one workflow for them end to end. Test the problem before you build the solution.
Score the problem first: the 4 U’s
Unworkable
It really hurts
Failure has serious consequences: lost revenue, risk, or someone’s job on the line.
Unavoidable
They can’t escape it
It comes with the role, the regulation or the market, every week.
Urgent
It’s a priority now
It competes well for their limited time, attention and budget this quarter.
Underserved
Nothing fits today
Existing options are too costly, too complex or simply missing.
Ship a thin slice, not a layer
What makes the MVP cut
- The one workflow that proves the core value for your segment
- Only the integrations that workflow can’t run without
- Human review wherever an AI error would be costly
- Simple tracking of the one or two metrics that prove value
- Everything else goes on the “later” list until usage earns it
Ask every user: “What are all the reasons you wouldn’t use this?” The value has to outweigh the pain of switching.
Validate before you scale
Problem interviews
10–20 conversations to confirm the pain, in their words, before any build.
Demand test
A problem-specific landing page and offer to see who actually signs up.
Concierge pilot
Deliver the outcome partly by hand to learn the real workflow.
Thin-slice MVP
Automate that one workflow end to end, with AI where it adds value.
Paid pilot
Prove the outcome with paying users and measure it.
Scale
Harden, automate and extend to adjacent workflows.
Each step earns the right to the next.
Small, senior teams with AI tooling can take a validated idea to a live pilot fast.
Engineer agents you can rely on
Moving from chatbots to agents that plan, use tools and cooperate changes how products must be built. We focus on four engineering pillars that separate dependable agents from fragile demos.
1 · Architecture: narrow scopes, clear roles
Goal
User or business outcome
Orchestrator
Supervisor agent
- Break big jobs into a chain of narrow steps; each model call does one thing
- Use a supervisor to plan and delegate to specialist workers as journeys grow
- Validate every tool input and output against a strict schema before it moves on
- Keep agents grounded in your documented processes and data
2 · Cost: prove it big, then step down
Illustrative: keep the cheapest option that stays above your quality floor.
- Prove the workflow on the most capable model first, then downgrade step by step
- Measure exactly where quality dips before swapping models
- Manage context actively: summarise past steps, drop what’s irrelevant
- Use retrieval for long-term memory rather than resending full histories
- Track cost per completed task, not just cost per call
3 · Trust: earn autonomy step by step
Level 1
Suggest
Shows its plan and recommends.
Level 2
Draft
Prepares the work for review.
Level 3
Act with approval
Executes after a human says yes.
Level 4
Act and report
Handles routine work, reports back.
Level 5
Autonomous
Within agreed limits, with escalation.
- Expose the agent’s plan so users see what it will do and why
- Self-heal routine errors; freeze and escalate high-stakes or unknown cases to a person
- Keep contracts, major spend and regulated advice human-only
4 · Quality: evaluate the whole journey
Test the trajectory, not just the answer
An output-only test sees just the last step. Trajectory evaluation checks every decision along the way, including AI-as-judge reviews.
Telemetry we wire in from day one
- Agent logs
- Latency
- Tool errors
- Cost per task
- Escalation rate
- Drift alerts
- User corrections
- Build evaluation sets from real cases, not just happy paths
- Use AI judges to check correctness, completeness and policy
- Stage against messy real-world data before going live
- Feed user corrections back into prompts, rules and evaluations
- Make small, frequent improvements guided by telemetry
Design AI into the product, not on top of it
The best AI features don’t feel like AI. Sparkle buttons and empty text boxes are giving way to AI woven into the workflow, where it prepares the work and the user stays in control.
Pattern
Inline actions
AI available exactly where the work happens, not in a separate chat window.
Pattern
Auto-fill and drafts
Forms, tables and documents pre-filled for the user to check and adjust.
Pattern
Insight panels
Summaries, risks and next steps surfaced alongside the task.
Pattern
Controls, not prompts
Presets, sliders and options replace blank text boxes.
Pattern
Review and confirm
Clear approve, edit or reject steps, with the reasoning visible.
Pattern
Easy to share
Outputs that become links, documents or actions others can use.
The moat isn’t the model
Everyone can call the same models. What’s hard to copy is what surrounds them: integrations, process templates, permissions, evaluations, reliability and the outcome data that builds up with every task. We design products around the outcome the customer wants, then build and measure the system that delivers it.
Goal
The outcome in the customer’s words.
Process discovery
Map how the work really happens today.
Agent design
Roles, steps, tools and guardrails.
Integrations
Connect the systems the work depends on.
Execution
Run it live on real work.
Human approvals
Oversight where the stakes are high.
Measurement
Hours saved, revenue created, costs avoided.
Optimisation
Improve continuously from the data.
From chatbot to measurable outcome engine.
Flexible ways to work with us
Outsourced
We deliver it
An Activo Group product lead and team deliver your MVP or agent on terms you agree.
New team
We build your team
Stand up a dedicated product and engineering team to add capacity as you need it.
Augmentation
We fill the gaps
Vetted specialists, such as AI engineers, embedded in your existing team.
Skills on tap
- Customer research
- POC & MVP delivery
- UI/UX design
- Testing & QA automation
- Business intelligence
- Data engineering
- AI & agent engineering
- Blockchain & Web3
Where we help
MVP scoping
Segment, problem score, value proposition and a thin-slice plan you can build against.
Prototype to MVP
Rapid, AI-assisted builds of digital products, with design and QA included.
Agent design and build
Supervisor and worker agents, tool integrations and strict schemas, built for production.
Evaluation and telemetry
Trajectory evaluations, AI judges and dashboards that catch drift before customers do.
AI cost engineering
Model baselining, context management and cost-per-task tracking.
Teams and talent
Outsourced delivery, new teams or AI engineers to augment your own.
Related services
Start today
Scope your MVP or first agent
Tell us the outcome you want and the workflow behind it. We’ll score the problem, define the thinnest slice worth building, and show how we’d make it reliable, measurable and affordable to run.
