Generative AI development: RAG, agents & LLM apps in production
AI development services from PoC to production — RAG systems grounded in your documents, AI agents that act inside your workflows, and LLM integrations with evaluation, guardrails and MLOps built in.
Industries we serve
Policy query deflection, fraud signal analysis, and compliance document search.
Clinical knowledge assistants and document processing pipelines grounded in your data.
AI-powered product recommendations and intelligent search that lifts basket size.
Supply chain intelligence and automated dispatch decision support.
Document extraction, reconciliation copilots, and risk scoring across financial workflows.
Adaptive learning assistants, content generation, and intelligent tutoring grounded in curriculum.
Predictive maintenance signals, ops copilots, and traveller-facing conversational assistants.
Quality inspection, downtime prediction, and shop-floor assistants for technicians and supervisors.
Why AI projects don’t reach production
AI needs more than accurate outputs — without secure data, explainability, and production hardening, projects stall before they ever reach real users.
AI demos built in isolation fail under real enterprise data volumes, edge cases, and concurrent users — never making it to production.
LLMs without RAG produce confident but inaccurate answers. Without evaluation pipelines, errors compound at scale and erode user trust fast.
AI that can't connect to your ERP, CRM, or knowledge base delivers limited value. Most vendors stop at the model layer — we don't.
Turning requirements into production AI
AI engineering services that understand your business, integrate with your systems, and deliver measurable outcomes — not just demos.
AI grounded in your organisation's knowledge
Turn complex unstructured data into usable intelligence — AI-driven extraction, review, and verification across documents, images, forms, and multimodal inputs.
- Document Processing — PDF, text, and multi-modal content ingestion with intelligent chunking.
- Semantic Search & Re-Ranking — Advanced vector similarity search with relevance re-ranking capabilities.
- RAG Implementation — End-to-end RAG system development from architecture to production deployment.
Engagement options
Engage us the way that fits your stage
Whether you’re exploring a first use case or scaling AI across the org, we shape the engagement around your team, timeline, and level of internal AI maturity.
AI Discovery Sprint
A 2-week engagement to identify use cases, audit data readiness, and ship a working prototype with a clear ROI case.
Project-Based Build
Fixed-scope delivery for a defined AI product — from architecture through production deployment and handover.
Dedicated AI Team
An embedded squad of AI engineers, ML ops, and product specialists working as an extension of your team.
Staff Augmentation
Senior AI talent plugged into your existing team to accelerate delivery on your in-flight initiatives.
From idea to production AI in weeks, not quarters
Tell us your use case. We’ll outline an approach, a timeline, and what good looks like — usually within 48 hours.
Start a conversationGo deeper
The practice, one topic at a time
Detailed guides to how we run each kind of AI engagement — what the phases are, how long they take and what decides the cost.
RAG development
How we build retrieval-augmented generation systems that answer from your documents with citations — ingestion, hybrid retrieval, evaluation, permissions and private deployment — and what decides the timeline and cost.
RAG DevelopmentPrivate LLM deployment
Open-weight models on your GPUs or in your cloud account — the three privacy tiers, choosing a model on your tasks, vLLM serving and GPU sizing, the security review, fine-tuning, and the cost model against the API.
Private LLM DeploymentAI agents
Agents that complete multi-step work inside your ERP, portal and ticketing systems — typed tools, LangGraph orchestration, human-in-the-loop approvals, evaluation on replayed cases and least-privilege guardrails.
AI Agent DevelopmentHow we deliver
From use case to production-ready intelligence
We remove the complexity around AI adoption — moving from a clear use case to a governed, monitored system running in production.
Scope
We pin down the highest-value use case and define a measurable success metric before any model work begins.
Data Audit
We assess quality, coverage, and access patterns across your sources to confirm the build is feasible.
Prepare
We clean, structure, and embed your data — building the retrieval and pipeline foundations the model will depend on.
Prototype
We build a working prototype against real data to validate the approach in days, not months.
Evaluate
We benchmark accuracy, stress-test edge cases, and add human-in-the-loop checks before scaling.
Deploy
We harden the system with guardrails, fallbacks, and security controls — ready for production traffic.
Monitor
We track drift, performance, and feedback loops so the system improves rather than degrades over time.
How we build responsible AI
Engineering AI Systems You Can Trust
Successful AI isn’t measured only by accuracy — it must be transparent, secure, and reliable. We embed responsible AI principles throughout the development lifecycle.
Responsible AI Practices
We establish measurable success criteria, validate models across diverse scenarios, and include human oversight to ensure quality, fairness, and reliability.
Explainable AI
Our systems provide visibility into how predictions and recommendations are generated — with explainability frameworks, monitoring, and governance reporting.
Evaluation & Monitoring
Every production AI system ships with evaluation benchmarks, drift detection, and feedback loops — so you know exactly how your AI performs over time.
Our approach
The building blocks behind every Gen-AI system
A modular framework — from architecture and prompt strategy to agents, tooling, and reusable assets — that shortens timelines and keeps systems extensible and traceable.
Layered Architecture
Modular Gen-AI design across data, instructions, intelligent systems, and assessment layers — engineered for extensibility and traceability.
Prompt Strategy
Beyond basic prompt design — systematic, distributable instruction frameworks with modular reasoning, fallbacks, and sector-specialised operations.
Agent Frameworks
Intelligent systems that merge LLM capability with your platforms using tools like LangChain and Semantic Kernel — reasoning, deciding, and acting across operations.
Technologies & Tools
Leading Gen-AI ecosystems, vector databases, advanced LLMs, orchestration layers, and monitoring frameworks for enterprise-grade deployments.
PoC Factory
Rapid experimentation with generative AI — fast design, feasibility checks, and outcome validation that lower the risk of adoption.
Reusable Components
A standardised asset library — including RAG workflows and evaluation frameworks — that accelerates every new implementation.
Popular models we deploy
The right model for the job
We’re model-agnostic by design. Every engagement starts by matching the right foundation model to your accuracy, latency, cost, and privacy needs.
GPT-4o / GPT-4
Strong general reasoning and multimodal performance for complex enterprise tasks.
Claude 3.5 Sonnet
Best-in-class long-context reasoning and instruction following for agent workflows.
Gemini 1.5 Pro
Massive context window for codebase, document, and video understanding.
Llama 3
Open-weight model ideal for on-premise deployments and full data sovereignty.
Mistral / Mixtral
Efficient open models tuned for low-latency production use at attractive cost.
Phi-3
Small, fast model for edge inference and embedded AI scenarios.
Qwen
Open multilingual model with strong performance across Asian languages.
Stable Diffusion / FLUX
Image generation models for creative, design, and visual content workflows.
Case studies
Analytics and AI systems we have put into production
CareEdge Analytics & Advisory
Early Warning System (EWS) for Banking Risk Analytics. ATS Global Techsoft designed and deployed a mission-critical Early Warning System (EWS) on a modern Angular + Spring Boot architecture.
Read case study
Public Sector BankingBank of Baroda — EWS Migration
Early Warning System Migration & Enhancement for Public Sector Bank. ATS led the complete migration to a Spring Boot + Angular architecture on Liferay DXP 7.4, introducing Angular portlets for dynamic UI, a multi-level maker-checker approval workflow, and predictive analytics modules for real-time financial risk flagging.
Read case study
From the blog
All articlesShift-Left Security for AI-Assisted Development
AI-assisted development increases the speed of code generation, making continuous security validation essential. This article explains five principles for shifting security left and securing AI agents throughout the development lifecycle.
Mohammed Izhaar Haq · SEP 20, 2026AI ENGINEERING ·Chunkless RAG: Why Flattening a Document Loses the One Thing That Made It Easy to Read
Chunk-and-embed RAG can lose the structure that makes long documents easy to navigate. Chunkless RAG preserves that structure and lets an agent reason through sections, headings, and relationships to find the right information.
Mohammed Izhaar Haq · AUG 10, 2026AI ENGINEERING ·Building production RAG systems: what we learned from five enterprise deployments
RAG demos are easy. Production RAG that stays accurate at scale is a different problem entirely. Here's what we learned building knowledge assistants for enterprise clients.
Mohammed Izhaar Haq · JUN 15, 2026FAQs
Common questions about custom AI
Which AI models do you work with?+
GPT-4o, Claude 3.5/3.7, Gemini, Mistral, and open-source models via Ollama or vLLM. We pick the best model for your use case and budget, not the one everyone is talking about.
Can you build AI features on top of our existing software?+
Yes. Most of our engagements involve augmenting an existing product by embedding an LLM into your workflow, adding a copilot, or wiring up a RAG pipeline to your existing data.
How do you handle data privacy and security?+
We can deploy entirely within your cloud environment so your data never leaves. For SaaS model APIs we use data processing agreements and can anonymise inputs before sending to any external model.
What is a typical AI project timeline?+
A production-ready AI feature typically takes 6-12 weeks from scoping to deployment. We validate feasibility in a 2-week technical spike before committing to a full build.
What does RAG mean and do I need it?+
Retrieval-Augmented Generation (RAG) lets an AI answer questions from your own documents and databases rather than making things up. If you want AI to know your products, processes, or data, you need RAG.
Let’s work together
Let’s build your AI system together.
Tell us your use case and where you are in your AI journey. We respond within one business day and can provide a customised quote for your requirements.