BUILD

Build the AI your business actually needs.

Tangible Labs combines product strategy, digital product design and AI engineering to turn a clear business opportunity into software people can use and teams can operate.

A product team shaping a digital experience together
01

Useful

Designed around a real user and business decision

02

Production-ready

Engineered beyond the prototype

03

Connected

Integrated with the tools you already use

04

Measurable

Tracked against adoption and business value

What this solves

What you get, in plain language.

Start with the business problem. We define and deliver the product, workflow or transformation needed to solve it.

At a glance

Come with an idea, a bottleneck or a half-built product.

We can shape the proposition, design the experience, engineer the intelligence, connect it to your systems and take it into production. Use Tangible as the complete AI design and engineering team or bring us into one critical part of the build.

Choose what you need built

  1. Custom AI Development

Bespoke AI software designed around a specific business workflow or opportunity.

  1. AI Product Development

Product strategy, UX, engineering and production delivery across the complete product lifecycle.

  1. Digital Product Design and UI/UX

Clear, high-conviction interfaces for AI, SaaS and complex B2B products.

  1. AI MVP and SaaS Development

Focused releases that test the proposition, reduce risk and create a credible path to scale.

  1. AI Agent Development

Agents that reason, use tools, take action and escalate to people when judgement is required.

  1. Voice and Conversational AI

Custom voice, chat and messaging experiences built for real customer journeys.

  1. RAG and Knowledge Systems

Grounded assistants that retrieve, interpret and answer from approved company knowledge.

  1. AI Applications and Platform Integration

AI capabilities connected securely to the applications, data and APIs your business already uses.

What this changes for you

  • Move from an idea or business problem to a usable AI system.
  • Reduce product risk before committing to a larger build.
  • Give users a clear, trustworthy experience around complex AI capabilities.
  • Connect intelligence to the systems and data required to produce an outcome.
  • Establish a production path that includes measurement, governance and human oversight.
  • Work with one accountable team across strategy, design, engineering and integration.

What we can deliver

  • Opportunity framing and product definition
  • User research, journey design and experience architecture
  • Product prototypes and interactive proof of concept
  • Custom AI applications and LLM-enabled workflows
  • AI agents, orchestration and human handoff
  • RAG, semantic search and enterprise knowledge assistants
  • Web applications, SaaS platforms and product interfaces
  • API, CRM, ERP, data and communication-platform integrations
  • Evaluation, observability and production-readiness planning
  • Product analytics and iteration roadmap

How we build

1. Frame the outcome

Define the user, business decision, workflow, target behaviour and evidence of success. This prevents a technology demo from being mistaken for a product.

2. Design the system

Map the experience, data, model behaviour, integrations, exceptions, security boundaries and human checkpoints before expensive engineering begins.

3. Build in working releases

Ship the smallest complete experience first. Test with real scenarios, expose failure states early and improve through visible releases.

4. Make it perform

Measure adoption, quality, response time, operating cost and business outcomes. Improve the system based on evidence rather than assumptions.

How it connects

We design around the problem and the client's environment. Depending on the use case, the stack may include leading language models, cloud AI services, vector databases, analytics tools and business-system APIs. Name OpenAI, Claude, Gemini, Azure OpenAI, AWS Bedrock or specific platforms only when they are genuinely part of Tangible's delivery capability. Present them as technologies we work with, not formal partners.

How we work

Small-team speed. Enterprise-level discipline.

You always know what is being decided, built and measured.

01

Frame

Define the outcome, user and riskiest assumption.

02

Prototype

Make the value tangible before committing to the full build.

03

Build

Design, develop and integrate the production system.

04

Improve

Measure adoption and strengthen what performs.

Built for reality

Connected. Governed. Ready to operate.

The new system should fit the business you already run, not create another isolated tool.

SystemsCRM · ERP · data · APIs
ControlsAccess · review · audit
PeopleRoles · handoffs · adoption
Product design concepts being reviewed in a collaborative workshop
Relevant exampleClient engagement

Make every workout plan more personal.

Conversational onboarding, adaptive routines and connected product analytics helped CloudFit turn a generic fitness journey into a more useful, measurable experience.

+45%sign-ups
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Read the case study ↗
FAQ

Questions about Build

Straight answers before you decide what to do next.

Can Tangible take an AI product from idea to production?

Yes. We can cover product definition, UX/UI, prototyping, AI and application engineering, integrations, testing and production planning. The exact engagement is shaped around what already exists and where the delivery risk sits.

Do we need a complete specification before approaching you?

No. A clear business problem or product hypothesis is enough to begin. We convert it into a prioritised scope, user journey, system design and release plan before full development.

Can you work with our internal product or engineering team?

Yes. Tangible can own the complete build or work as a specialist product, design or AI engineering layer inside an existing team.

How do you choose the right AI model and architecture?

We compare quality, latency, cost, security, integration constraints and the consequences of an incorrect response. The model is one component of the system, not the product strategy.

How do you manage AI risk during development?

We define approved data sources, access rules, human checkpoints, evaluation scenarios and fallback behaviour as part of the system design. Higher-risk use cases receive tighter controls and clearer escalation paths.

How long does a typical engagement take?

A focused diagnostic or prototype can take a few weeks. A production build or wider transformation is phased around complexity, integrations, risk and the evidence required at each gate.

What determines the cost?

Scope, workflow complexity, data readiness, integrations, security requirements and the level of production support determine investment. We define the smallest credible first phase before proposing a wider programme.

Can this work with our existing systems?

Usually, yes. We design around your current CRM, ERP, data, communication and operational tools, then recommend replacement only when an existing constraint genuinely blocks the outcome.

How do you handle security and human oversight?

We define access, approval, escalation, audit and monitoring requirements with the workflow. High-impact decisions retain the right human checkpoints and visible accountability.

Have a problem worth fixing?

Tell us what needs to change.

We will help you find the clearest route from business need to working system.

Talk to our team