Services

End-to-end capability from technical consulting and product engineering to AI engineering and data analytics, delivered together or one at a time.

Technical Consulting

Strategic AI Advisory

Clear AI strategy, cloud & DevOps modernisation and digital transformation, guided end-to-end.

How it runs
  1. 1Current state
  2. 2Opportunities
  3. 3Prioritise
  4. 4Target architecture
  5. 5Roadmap
  6. 6Execute and advise
Technologies
AWSAzureGCPTerraformKubernetesGitHub ActionsTOGAFFinOps

AI strategy

Align AI initiatives with business goals and aim for results, not stalled pilots.

A prioritised, costed plan for where AI creates value first, sequenced by value rather than hype. It aligns AI initiatives with business goals and aims for measurable results, not stalled pilots.

Typical use
  • Enterprise AI strategy
  • Build-vs-buy decisions
  • Use-case sizing

ExampleA telecom operator built a three-year AI roadmap in 30 days, which secured board approval and budget.

  • Strategy
  • Use-case sizing
  • Business case
  • Roadmap

Cloud and DevOps advisory

Cloud systems built for scale, security and performance, delivered faster.

Cloud systems designed for scale, security and performance, with shorter, safer paths from code commit to production. FinOps practices give you visibility and control over cloud cost.

Typical use
  • Cloud migration planning
  • DevOps acceleration
  • FinOps cost visibility

Example scenarioA team shortens the path from code commit to production and sees its cloud spend by product.

  • Cloud
  • CI/CD
  • FinOps
  • Security

Digital transformation

Modernise processes, systems and operations around business goals.

Technology investment is aligned with business goals, and processes, systems and operations are modernised in order of business value, with change management built in.

Typical use
  • Transformation roadmaps
  • AI operating model
  • Process and change management

Example scenarioProcess and system upgrades are sequenced by business value, so the first wins arrive quickly.

  • Modernisation
  • Operating model
  • Process
  • Change

What you receive

An assessment, sized opportunities, a business case, a target architecture and a sequenced roadmap.

A delivery plan your own teams can run, not just a presentation. Every engagement ends with a current-state assessment, sized opportunities, a business case, a target architecture and a sequenced roadmap.

Typical use
  • Assessment
  • Business case
  • Target architecture

Example scenarioA leadership team starts from a current-state assessment and ends with a sequenced roadmap in about 30 days.

  • Roadmap
  • Operating model
  • Sized opportunities
  • Delivery plan
Product Engineering

From concept to production

Discovery, UX, rapid prototyping and full-stack delivery market-ready products with AI at the core.

How it runs
  1. 1Discovery
  2. 2UX and prototype
  3. 3Architecture
  4. 4MVP build
  5. 5Scale and harden
  6. 6Iterate
Technologies
ReactNext.jsTypeScriptNode.jsReact NativeFlutterPostgreSQLFigma

Discovery and strategy

A clear vision, roadmap and business case grounded in your users.

An idea is shaped into a clear vision, strategy and roadmap, grounded in user research and a business case. The problem, the users and the case are tested before any code is written, so weak ideas are filtered out early and cheaply.

Typical use
  • AI-native SaaS platforms
  • Rapid MVP validation
  • Business case for a new product

Example scenarioA founder tests a product idea with real users and a costed roadmap before committing to a build.

  • Vision
  • Research
  • Roadmap
  • Business case

UX/UI and prototyping

Designs validated with clickable prototypes before engineering starts.

Experiences are designed and checked with real users through clickable prototypes before engineering starts. You see and try the product early, when changes are cheap.

Typical use
  • Customer portals
  • Design systems
  • Mobile app interfaces

Example scenarioA clickable prototype is tried by real customers, and what they say shapes the build before engineering starts.

  • UX research
  • UI design
  • Prototypes
  • Design system

PoC and MVP

Working prototypes tested with real users and iterated toward launch.

A testable slice ships quickly, then you learn from real usage and iterate toward a market-ready product. Ideas are proved or disproved in weeks, with real users and real data.

Typical use
  • Rapid MVP validation
  • Internal tooling
  • Proving an idea with real data

ExampleAn AI-powered logistics MVP went live in six weeks, routing thousands of shipments from day one.

  • PoC
  • MVP
  • User testing
  • Iteration

End-to-end development

AI-powered web, mobile and desktop apps, built and supported past launch.

AI-powered web, mobile and desktop applications, built, hardened and supported through launch and beyond. Performance, security, observability and CI/CD are part of the build, with monitoring and iteration after go-live.

Typical use
  • Mobile apps
  • Legacy modernisation
  • Customer portals

Example scenarioA customer portal is built, hardened and supported past launch, and lightens the load on the support team.

  • Web
  • Mobile
  • Desktop
  • DevOps
AI Engineering & Deployment

Core AI Engineering & Deployment

Production-grade AI systems custom apps, intelligent agents, ML, computer vision and NLP, engineered end-to-end.

How it runs
  1. 1Assess data
  2. 2Design architecture
  3. 3Build and fine-tune
  4. 4Evaluate
  5. 5Deploy
  6. 6Monitor
Technologies
LangChainHugging FacePyTorchTensorFlowVector databasesDockerKubernetesAWSAzureGCP

AI applications

Custom apps built around your data and workflows.

We build custom AI applications around your data and workflows, from retrieval-augmented search to LLM integrations and APIs. Teams get smarter decisions inside the tools they already use, instead of another system to learn.

Typical use
  • Document intelligence
  • Personalisation engines
  • Internal knowledge assistants

Example scenarioA legal team asks questions across thousands of contracts and gets answers with the source clause cited.

  • Custom apps
  • RAG
  • LLM integration
  • APIs

AI agents

Agents that automate multi-step business processes.

Agents reason through a task, call the right tools and hand off between steps. We orchestrate them into workflows that run multi-step business processes, with people reviewing the exceptions.

Typical use
  • Autonomous back-office workflows
  • Process automation
  • Request triage and routing

Example scenarioAn agent reads incoming supplier invoices, matches them to orders and flags only the mismatches for a person.

  • Autonomous agents
  • Tool use
  • Orchestration
  • Workflows

Machine learning

Custom models trained on real-world data to automate decisions.

Custom models for classification, regression and recommendations, trained, validated and tuned on your real data. They go into production with MLOps, so accuracy is monitored and holds up over time.

Typical use
  • Predictive decisioning
  • Recommendations
  • Risk and opportunity scoring

Example scenarioA sales team ranks its open leads by likelihood to close, so effort goes where it counts.

  • Classification
  • Regression
  • Recommenders
  • MLOps

Computer vision

Detection, recognition and quality inspection from images and video.

Models that read images and video for detection, recognition, OCR and inspection, at scale and in real time. Results flow back into the systems your operations team already uses.

Typical use
  • Visual quality inspection
  • Document capture
  • Video analytics

Example scenarioA production-line camera spots surface defects as products pass and flags them before packing.

  • Object detection
  • OCR
  • Inspection
  • Video AI

Natural language processing

Turning unstructured text into insights.

NLP works across the unstructured text your teams handle every day: extracting fields, classifying, summarising and searching by meaning. Results feed straight into your systems of record.

Typical use
  • Document intelligence
  • Customer feedback analysis
  • Contract review

ExampleA bank cut document processing time by 70% with an NLP pipeline that reads, classifies and routes thousands of forms daily.

  • Extraction
  • Summarisation
  • Sentiment
  • Semantic search

AI virtual assistants and chatbots

Assistants that resolve queries and reduce support load around the clock.

Always-on assistants, including voice and multilingual ones, that resolve queries, guide users and reduce support load around the clock. Routine questions are settled instantly and complex ones hand off cleanly to a person.

Typical use
  • Intelligent support automation
  • Internal help desks
  • Voice and multilingual service

Example scenarioA support assistant answers routine first-line questions at any hour and passes the rest to an agent with the full context.

  • Chatbots
  • Voice AI
  • Support
  • Multilingual
Data & Analytics

Intelligence from Data

Advanced analytics, predictive AI and real-time dashboards that turn raw data into strategic advantage.

How it runs
  1. 1Discover and audit
  2. 2Design pipelines
  3. 3Integrate
  4. 4Model and analyse
  5. 5Visualise
  6. 6Monitor
Technologies
SnowflakeDatabricksdbtAirflowKafkaPower BITableauPythonPostgreSQL

Analytics and visualization

Dashboards and real-time reporting that get answers to the people who need them.

Dashboards and reports refresh continuously instead of monthly, built in tools such as Power BI and Tableau and embedded where teams already work. Governed access means people get answers without waiting on IT.

Typical use
  • Real-time executive dashboards
  • Self-serve analytics with governed access
  • Operational reporting

Example scenarioA leadership team opens one live dashboard each morning instead of waiting for a month-end pack.

  • Dashboards
  • BI
  • Real-time
  • Embedded reports

Advanced analytics and AI

Predictive models and machine learning that uncover trends and explain drivers.

Predictive models and machine learning explain what drives results and where growth is. We turn fragmented records into answers to specific business questions.

Typical use
  • Customer 360 views
  • Churn and risk scoring
  • Driver analysis

Example scenarioA subscription business flags accounts likely to leave early enough for the team to act.

  • Predictive models
  • ML
  • Forecasts
  • Insights

Data migration and integration

Connect, consolidate and migrate data into one governed foundation.

Systems are connected and consolidated into one governed foundation, whether a warehouse, lakehouse or lighter pipeline fits best. We work with stacks such as Snowflake, Databricks, dbt, Airflow, Kafka and PostgreSQL on AWS, Azure or GCP.

Typical use
  • Warehouse modernisation
  • Legacy to governed cloud platforms
  • Customer 360 data foundations

Example scenarioA legacy reporting database moves to a governed cloud platform with no downtime for the business.

  • ETL/ELT
  • Pipelines
  • Warehousing
  • APIs

Forecasting

Forecasts that report honest confidence intervals, not false precision.

Forecasts are built on cleaned and enriched data, even when history is short or messy, and report honest confidence intervals rather than false precision. Models are chosen for the quality of the history available.

Typical use
  • Demand, stock and staffing forecasts
  • Risk scoring
  • Time series on sparse data

ExampleA retailer cut forecasting error by 35% with a pipeline that reads sales, seasonality and promotions to plan stock weeks ahead.

  • Demand planning
  • Risk scoring
  • Time series
  • Confidence intervals

Innovation lab

SaturnIQ Forge

Co-innovate, prototype and scale with confidence.

Our dedicated AI innovation lab. Enterprise teams move from idea to impact quickly, with the tools, expertise and technical environment to experiment safely.

Book a demo

  • Rapid MVP development

    Turn an idea into a working product fast.

  • AI testbed environment

    A safe place to try models and ideas.

  • Reusable component engineering

    Building blocks you can carry into production.

  • Joint R&D projects

    Co-innovation with our engineers.

Built for
  • Innovation heads and CTOs
  • Product leaders
  • Strategy and digital transformation teams

Education

AI Labs

AI and robotics labs that actually get used.

A managed Lab-as-a-Service for schools and colleges: hardware, curriculum, teacher development, annual updates and industry and university connections.

Book a demo

  • AI and Robotics Lab

    For CBSE schools, Grades 4 to 12.

  • Physical AI Lab

    For engineering colleges, BTech Years 1 to 4.

  • Axoniq Learn

    Our learning platform, included with both programmes.

  • Lab ready in 6 to 9 weeks

    From agreement to a working lab.

Aligned with
  • CBSE CT and AI aligned
  • ATL compatible

Have something to build?

Tell us the problem. We will come back with a plan.

Book a demo