Generative AI applications
AI copilots, content and workflow tools, internal knowledge assistants, and domain-specific interfaces that help users create, decide, and act faster.
AI Solutions
Custom AI Development
AI systems engineered for your specific product and data — not a wrapper around someone else’s API.
Grounded in your data
Integrated into your product
Evaluated before launch
Monitored for cost and quality
DataDocuments, records and events
ModelsLLMs, embeddings and prediction
ProductApps, backends and workflows
EvaluateAccuracy, cost and latency
CUSTOM
AI
SYSTEM
AI
SYSTEM
What it is
AI capability built around your product, domain, and operating reality.
Custom AI development covers AI systems tailored to your product, data, and operational requirements: generative AI applications, retrieval-augmented systems, predictive models, and AI features embedded directly into your existing software.
Instead of treating an LLM as a generic answer engine, we design the data pipeline, model strategy, evaluation method, permissions, user experience, and operational controls needed for the AI feature to work reliably in its intended environment.
What we build
AI products and capabilities engineered around a real use case.
We design the system around the outcome, the relevant data, the people using it, and the platform it must fit into.
RAG systems
Retrieval-augmented generation systems that connect approved documents and enterprise data to an LLM, with source-aware answers and controlled retrieval.
Predictive and analytics models
Forecasting, anomaly detection, risk scoring, classification, and decision support built on relevant operational data and business logic.
Embedded AI and LLM integration
Search, recommendations, summarisation, classification, fine-tuning where appropriate, and other AI features built directly into existing products and platforms.
Our delivery approach
From architecture to live product performance, with evaluation built in.
We make the critical design choices early, test against defined benchmarks, and keep cost and maintainability visible throughout delivery.
Architecture design
Select the model approach, data pipeline, retrieval pattern, infrastructure, access controls, and product integration path before development begins.
Data preparation
Clean, structure, secure, and govern the documents, records, events, and metadata the system will rely on for useful output.
Iterative development
Build in reviewable milestones so product, domain, and engineering stakeholders can test a working system rather than wait for a black-box delivery.
Evaluation and hardening
Test accuracy, hallucination behaviour, retrieval quality, latency, edge cases, permissions, and failure handling against defined benchmarks.
Deploy and optimise
Launch with production monitoring for usage, output quality, infrastructure cost, latency, and the improvements required as real users interact with the system.
Representative applications
Representative patterns for custom AI delivery.
The specific model and interface change by use case. The engineering pattern remains focused on reliable data, practical integration, and measured product value.
01
Source-grounded knowledge copilot
A secure internal assistant retrieves from approved policies, manuals, customer records, or product documentation and shows the source context behind its answer.
RAG and knowledge systems02
AI feature embedded in a SaaS product
A product-native assistant helps users search, classify, summarise, recommend, or generate content inside the workflow they already use, with permissions inherited from the core platform.
Product AI03
Operational forecasting and risk signals
A tailored model analyses historical patterns and current operational data to identify anomalies, forecast demand, score risk, or surface decisions that need human attention.
Predictive intelligenceEvidence-led delivery
Success is measured against a baseline, not assumed.
We set the relevant outcome metrics before build or rollout, track them in production, and use the resulting evidence to improve the next release. We do not publish a universal “success rate” without verified client data and approval.
Evaluate outputs against representative test cases, including factual grounding, task completion, and user review criteria.
Track source retrieval, unsupported claims, permission behaviour, escalation patterns, and high-risk output handling.
Monitor model selection, token usage, caching, infrastructure cost, response time, and performance under realistic demand.
Measure where users adopt the capability, where they disengage, and whether the feature improves the intended product workflow.
Who this is for
For product and engineering teams that need AI built into the systems they own.
This service is for teams that need more than a generic chatbot: a data-grounded assistant, a predictive workflow, a product-native copilot, or an AI capability that integrates with their backend, database, cloud environment, and user permissions.
Frequently asked questions
Custom AI development questions, answered.
Practical guidance on how custom systems differ from generic tools, who owns the work, and how the system remains economical in production.
Off-the-shelf tools are designed for generic data and generic workflows. Custom AI development builds a system around your specific data, product logic, integrations, permissions, and cost requirements. It gives you more control over grounding, user experience, intellectual property, and the way the AI operates inside your workflow.
The client owns the IP for the custom system Nonceblox builds for them. Ownership, deliverables, and any third-party components are established contractually before development begins.
We match model selection to task complexity rather than defaulting to the most expensive model, and design for caching, prompt efficiency, retrieval quality, usage monitoring, and sensible fallbacks. Cost visibility is built into the system from the first production release.
Yes. Custom AI systems are designed to integrate with existing backends, databases, cloud infrastructure, user permissions, and product interfaces wherever practical. The architecture is selected around the environment you already operate.
Build the AI capability your product actually needs.
Bring your product context, data constraints, and intended user workflow to a discovery call. We will help you define an architecture and delivery path that is credible before development begins.
AI solutions
Explore the complete AI practice.
Each service can stand alone or become part of one coordinated AI roadmap.