Solutions & Products

NKK Innovations designs, builds and launches AI solutions for real workflows, and builds its own AI products too. Every engagement follows the same path, with a decision at each stage.

Tell us your problemSee the six solutions
  1. Discover
  2. Build
  3. Validate
  4. Scale

Solutions

Six kinds of AI we build for real work

Each starts from a business problem and ends in something your people use. Pick the one closest to yours, or tell us the problem and we will help you choose.

Intelligent Automation

AI agents that run knowledge-intensive workflows with context and judgment.

AI agents read a request, use the right tools and make a bounded decision: resolve it, or route it to a person with the context attached. They take over the repetitive judgement that slows knowledge work down.

Typical use cases

  • Document-heavy intake and triage
  • Back-office processing with exception routing
  • Internal requests across IT, HR and finance
  • Research and report assembly from many sources

How we deliver it

  1. Map the workflow. Find the decision points, the systems involved and where a person must stay in charge.
  2. Build with guardrails. Give the agent tools, limits and clear hand-offs to people.
  3. Evaluate, then widen. Measure on real cases and extend autonomy only as the evidence supports it.

What good looks like

  • Routine handling moves off people's desks
  • People focus on the exceptions that need judgement
  • Every automated decision can be traced
  • Agentic AI
  • Workflow orchestration
  • Human in the loop
  • Evaluation
Discuss intelligent automation

Conversational AI

Chat and voice assistants that understand your business domain.

Chat and voice assistants that answer from your own approved knowledge and know when to hand over to a person. They are designed around your domain, vocabulary and tone, not a generic chatbot.

Typical use cases

  • Customer support and self-service
  • Employee help desks and onboarding guides
  • Sales and product assistants
  • Voice assistants for call centres and field teams

How we deliver it

  1. Ground in your sources. Connect approved content so answers come from your knowledge, with sources shown.
  2. Design the conversation. Shape tone, scope and the hand-off to a person when the assistant should stop.
  3. Test and monitor. Run it against real questions before launch and keep quality tracked afterwards.

What good looks like

  • Answers grounded in your own content
  • A clean hand-off when a person is needed
  • Quality that is measured after launch, not assumed
  • Retrieval-augmented generation
  • Large language models
  • Voice
  • Evaluation
Discuss conversational ai

Predictive Intelligence

Forecasting and anomaly detection that surface what to act on.

Forecasting and anomaly detection that tell your team what is likely to happen and what looks wrong, tied to a decision someone owns. The point is the action it informs, not the model.

Typical use cases

  • Demand and inventory forecasting
  • Anomaly detection in operations and finance
  • Risk scoring and prioritisation
  • Capacity and maintenance planning

How we deliver it

  1. Define the decision. Start from the decision the prediction should improve, then check the data can support it.
  2. Model against baselines. Back-test against simple baselines and show uncertainty, not just a single number.
  3. Deploy and watch for drift. Put monitoring in place so you know when the model stops matching reality.

What good looks like

  • Forecasts that show their uncertainty
  • Alerts routed to a named decision owner
  • Models monitored for drift after release
  • Forecasting
  • Anomaly detection
  • MLOps
  • Monitoring
Discuss predictive intelligence

Search & Discovery

Semantic search and recommendations over your own knowledge.

Semantic search and recommendations over your own documents, tickets, products and records. People ask in plain language and get the right material, with permissions respected.

Typical use cases

  • Enterprise knowledge search
  • Policy, contract and regulation lookup
  • Product and content recommendations
  • Technical documentation and case similarity

How we deliver it

  1. Structure the content. Index sources, clean them up and carry access rules through to the results.
  2. Tune retrieval on real queries. Combine keyword and semantic search, and re-rank against questions people actually ask.
  3. Add answers and feedback. Return cited answers and learn from what people click and correct.

What good looks like

  • People find the right material faster
  • Results respect who is allowed to see what
  • Answers point back to their sources
  • Vector search
  • Hybrid retrieval
  • Embeddings
  • Re-ranking
Discuss search & discovery

Computer Vision

Visual AI for inspection, documents and scene understanding.

Visual AI that reads images, scans and documents: finding defects, extracting fields, recognising objects. Built with a review step where the cost of a mistake is high.

Typical use cases

  • Quality inspection on production lines
  • Document and form extraction (OCR)
  • Safety and compliance checks on imagery
  • Asset, shelf and inventory recognition

How we deliver it

  1. Gather representative images. Collect and label examples that reflect real conditions, including the difficult ones.
  2. Evaluate against error costs. Measure what a miss and a false alarm each cost, then tune for that balance.
  3. Deploy with a review queue. Run at the edge or in the cloud, with uncertain cases sent to a person.

What good looks like

  • Inspection and extraction that run consistently
  • Uncertain cases routed to people, not guessed
  • A model tuned to the cost of being wrong
  • OCR
  • Object detection
  • Classification
  • Edge deployment
Discuss computer vision

Custom AI Products

From concept to a deployed, user-facing AI application.

End-to-end development of AI products: the data layer, the model layer and the interface people use. For teams that want a product their organisation owns and can run.

Typical use cases

  • Internal AI copilots for specific roles
  • AI features inside an existing product
  • Data products and APIs
  • Agentic platforms and new AI ventures

How we deliver it

  1. Shape the product. Define the user, the job to be done and the data it will stand on.
  2. Build all three layers. Engineer the data, the model and the interface together, so they fit.
  3. Launch and iterate. Release to real users, learn from usage and hand over a documented foundation.

What good looks like

  • A product your team owns and can run
  • Documentation and knowledge transfer included
  • A foundation ready to extend, not a one-off demo
  • Full-stack AI
  • APIs
  • Product design
  • Platform engineering
Discuss custom ai products

How we work

Rapid PoC to production in four stages, with a decision at each

You can stop, adjust or continue at every gate. We would rather tell you early that an idea will not work than build it anyway.

Tap a step to jump to its details below

Stage 1 of 4: Discover

Clarify the problem before building anything

A focused session on the problem, the data and what success looks like. We leave with a scoped problem and a clear view of whether it is worth building.

  • Scoped problem and success criteria
  • A read on data and risk
  • A go or no-go to build

Stage 2 of 4: Build

A working prototype by the fastest sound route

Our engineers build something you can use, not a slide. It is the shortest path to demonstrable value, with evaluation designed in from day one.

  • A working prototype
  • An evaluation set on your own cases
  • A sketch of the production architecture

Stage 3 of 4: Validate

Test with real users and real data

We check the prototype against the criteria agreed at the start, including governance and security questions. The result is a decision: scale, adjust or stop.

  • Findings against the agreed criteria
  • A risk and governance review
  • A clear decision: scale, adjust or stop

Stage 4 of 4: Scale

Take it to production, or hand over a clean base

Once validated, we productionise it with monitoring and controls, or hand your team a documented foundation they can run themselves.

  • Production engineering and monitoring
  • Documentation and knowledge transfer
  • A foundation your team can extend

Engagement models

Start small, or bring a team in

Scope, timeline and team are agreed at the start of each engagement, based on your problem.

Rapid PoC

Prove the concept with a focused, scoped prototype before committing to a build.

Best when: You have a use case and need evidence.

  • Problem scoping session
  • Working prototype
  • Validation report
  • Hand-off package
Talk about Rapid PoC

Build & Launch

Take a validated concept to a production-ready AI system.

Best when: The concept works and needs to ship.

  • Everything in Rapid PoC
  • Production engineering
  • MLOps and monitoring setup
  • User acceptance testing
  • Launch support
Talk about Build & Launch

Embedded AI Team

An AI team that works as part of your organisation, on a continuing basis.

Best when: AI is an ongoing capability, not a single project.

  • Dedicated AI engineers
  • Regular delivery cadence
  • Full delivery ownership
  • Knowledge transfer to your team
Talk about Embedded AI Team

Products built by NKK

We do not only advise on AI. We build AI products.

Through the NKK venture studio we design, build and operate our own products. They keep our engineers close to the real work of shipping AI, and they show what we mean by building. About the venture studio.

B2B Trade Network

NEWOXX

The infrastructure global B2B trade runs on, not another directory.

Explore NEWOXX

EdTech

Pra-PATH

Proof of real understanding, in a world where AI content is free.

Explore Pra-PATH

Relationship AI

Followup

Never let a meaningful connection quietly fade.

Explore Followup

Travel Memories

MeMi

Trips become stories, made together with the people who were there.

Explore MeMi

More products coming

The venture studio keeps building. New NKK products will appear here as they are ready to be shown.

Talk to the venture studio

FAQ

Questions we hear before a first call

What is the difference between a proof of concept and a pilot?

A proof of concept answers one question: can this work on our problem and our data? A pilot puts a working version in front of real users to learn how it behaves in practice. We scope each separately so you can stop, adjust or continue at every gate.

Does our data need to be ready before we start?

No. Discovery includes a read on the data you have. Many projects begin with a narrow, well-understood slice of data and widen from there, rather than waiting for a perfect foundation.

Which models and cloud platforms do you work with?

We are model- and cloud-agnostic. We choose based on accuracy, latency, cost, data residency and your existing stack, and we design so a model can be swapped later without rebuilding the product.

How do you handle security, privacy and governance?

We design for them from the start: access controls, data handling choices, logging and human review where the risk calls for it. We work to your policies and can align with frameworks such as the NIST AI Risk Management Framework or ISO/IEC 42001 where they apply. Certification of your organisation is a separate process.

Who owns what gets built?

Ownership and licensing terms are agreed in the engagement terms before work starts. Our aim in every engagement is a documented foundation that your team can run and extend.

What happens after the proof of concept?

That depends on the validation result. If it meets the agreed criteria we productionise it or hand it over. If it does not, you have learned that cheaply, and we will say so plainly.

How do the NKK products relate to your client work?

NEWOXX, Pra-PATH, Followup and MeMi are products built by the NKK Innovations venture studio. They are separate from client engagements, and they keep our team close to the real work of designing, building and shipping AI products.

Bring us the problem. We will tell you if AI is the answer.

A short conversation is enough to scope a first step. If a proof of concept is not the right move, we will say so.

Start a conversationSee our products