AI Development
The design, build, and operations that take AI from prototype to production
From scattered AI pilots to production-grade systems that scale
We help you to:
Assess your AI readiness before you commit budget or people
Identify and prioritise the use cases with the clearest return
Design the solution architecture that fits your systems and data
Build agentic systems, engineering practices, and the data platform behind them
Keep AI systems reliable and cost-controlled as usage scales
AI Development Services
AI Readiness Assessment
A structured review of your data, teams, and systems, pinpointing exactly where AI can add real value and what needs fixing before you build.
Use Case Prioritisation
Your AI opportunities ranked by value, risk, and effort, so the first investment lands somewhere that genuinely moves the business.
AI Solution Design
The technical blueprint for your AI system: architecture, data flows, model choices, and integration points, mapped out before a single line of code gets written.
Agentic Systems
Single agents or coordinated multi-agent systems, engineered to plan, reason, and act inside your real tools, not a demo environment.
AI-Native SDLC
AI embedded across planning, development, testing, and release, so engineering teams ship faster without losing quality or control.
AI Data Platform
The data foundation your AI actually needs: clean, structured, and enriched so every model and agent works from accurate information.
AIOps & Infrastructure
Ongoing visibility into cost, performance, and reliability once your AI systems are live, so problems and runaway spend get caught early.
What it takes to run AI at enterprise scale, engineered in from the start
A production AI system has to work against real data. It needs to integrate with the systems you already run, handle concurrent load, and keep performing when inputs are messy or unexpected.
At Zartis, we approach AI development the way we engineer any enterprise system: architecture designed for your actual data and integration constraints, testing and fallback behaviour built in from the start, and cost and reliability treated as design decisions.
Our GenAI Engineers and Architects own the system from architecture through to production and manage how it performs once real usage arrives. The result is AI that scales with your business instead of needing a rebuild the moment it succeeds.
Our AI Navigators help you implement solutions with daily support, and bring every team along at the same pace. The result is reliable enterprise products that can scale.
The Zartis Way
AI you can trust and control.
Shaped in the lab and proven through real-world experience.
Frameworks
- Zartis Laws of Enterprise AI
- ATOM: Proprietary AI Trust Operating Model
- AI Maturity Matrix
Tooling
- Zartis Academy
- Z-CORA: Proprietary Claude Plugin Family
- Z-Forge: Agentic Harness with Trust Score
Unique IP
- Skills 3.0: Heuristics & Praxis
- Agentic Trust Architecture
- Cognitive Units
Z-CORA
Zartis Cognitive Orchestrated Reasoning Architecture
Our proprietary Claude plugin family built to boost productivity, quality, and security, while managing costs.
Z-CORA is future-proof, tool-agnostic, and backed by 2000+ years of accumulated software experience and cutting-edge AI research.
Get access to our custom Claude Code & Cowork plugin, adding trust and control to AI-driven coding through human-in-the-loop workflows.
What you get:
- Auto-generate PRDs and user stories
- Smart code generation with latest libraries
- Automated testing (BDD/Gherkin)
- AI-assisted design and architecture reviews
Our Capabilities
Range of expertise.
Depth of delivery.
Model Fine-Tuning
Refining existing foundation models to perform better on your domain-specific data and tasks.
Custom Model Training
Training lightweight or specialised models when your use case requires full control or unique behaviour.
Developer AI Tools
Implementing AI-powered tooling that accelerates development, improves quality, and enhances the developer experience.
AI Coding Assistants
Bringing safe, structured, and compliant AI coding support into your SDLC to boost engineering productivity.
Predictive Analytics
Using statistical models and machine learning to forecast trends, detect patterns, and inform business decisions.
Computer Vision
Building image and video understanding capabilities for classification, detection, tracking, and inspection workflows.
AI Audit Systems
Designing automated systems that review AI outputs for accuracy, quality, and adherence to policy.
Safety & Compliance
Ensuring AI systems meet regulatory, ethical, and organisational standards through controls and governance.
FAQs: AI Development
What does AI Development at Zartis actually involve?
It covers the full engineering path from idea to production. We assess readiness, prioritise use cases, and design the solution architecture. Then, we build agentic systems, GenAI features, the data platform behind them, and the AI-native SDLC practices that keep teams shipping quickly.
AIOps and FinOps carry that into ongoing operation once the system is live. Most engagements combine several of these depending on where you are starting from.
How is this different from AI Agent Development?
AI Agents is the dedicated line for building single agents and multi-agent systems, and it sits inside the wider scope of AI Development. If your need is specifically agentic, that page goes deeper on architecture and orchestration. If you need broader AI engineering, GenAI features, data foundations, or SDLC practices alongside agentic work, AI Development is the right starting point.
Do we need the AI Readiness Assessment before we can start building?
Not always. It is recommended if you have not already prioritised your use cases or confirmed your data is workable, since skipping that step is the most common reason AI projects stall midway. If you already have a clear use case, clean data, and buy-in, you can move straight into solution design.
How do you handle hallucinations and inconsistent outputs in production?
These get treated as engineering constraints from the start, not edge cases discovered after launch. Guardrails, evaluation pipelines, fallback logic, and monitoring are built into the system design, so failures are caught and handled predictably rather than surfacing unexpectedly in front of users. This is the same discipline applied across GenAI, agentic, and data platform work.
Can you build on our existing data and systems, or do we need to rebuild everything?
Almost always the former. The AI Data Platform work sits as a layer between what you already have and the AI applications you need, so there is rarely a need to rebuild data infrastructure from scratch. The same applies to integrations: agentic systems and GenAI features are built to connect to your existing tools, not replace them.
Who actually works on the project? Our team, yours, or a blend?
This is agreed during scoping and can shift as the project matures. Some clients want us to handle everything while others prefer embedded Zartis experts. Some need specific specialists such as GenAI architects, AI Navigators, Data Engineers, or MLOps Engineers – embedded into their development teams. All models are common, and it is rarely an all-or-nothing decision. Depending on your needs, you can open a conversation and scale your team flexibly.
Why Zartis?
We transform AI ambition into an operating capability across your organisation, people and products, and help you reach beyond a collection of isolated tools or pilots. Most consultancies advise and most engineering firms build; we do both, and we connect them. That’s backed by a genuinely global delivery model, giving you dedicated teams that scale with the engagement.
We’re also a Preferred Services Partner in the Claude Partner Network, and we apply the same production discipline in governance, reliability and cost control to every model we deploy.