AI that ships —modelled, engineered, monitored.
Enterprise AI development services covering custom models, RAG pipelines, copilots and AI-enabled software — integrated with your data, evaluated for real-world use and governed by the same team that engineers and operates it.
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Full LLM / Model Dev
Custom model development — fine-tuning, RAG, evaluation and the operational pieces that make a model production-safe.
Full LLM / Model Development
Training · Fine-tuning · RAG
For teams building on top of foundation models — or training their own. We handle model selection, fine-tuning, retrieval pipelines and rigorous evaluation, so what ships behaves the way you claimed it would in the pitch deck.
- Foundation-model applications
- Fine-tuning on proprietary data
- RAG & retrieval architectures
- Evaluation & guardrails
- Prompt & agent orchestration
- Custom model training
AI Compute
Compute sized to the workload, not the spec sheet.
AI compute is a means, not a menu. We start from what you're trying to run — a copilot in production, a fine-tune every fortnight, a render farm, a foundation-model cluster — and match hardware, interconnect and orchestration to that shape. Pricing is per workload, on quote.
Inference & Serving
Real-time model serving
For chatbots, in-product copilots and API-served models where latency and cost per request matter more than raw throughput. Cost-efficient, predictable, and sized to steady production traffic.
Training & Fine-Tuning
Adapt a model to your data
For teams fine-tuning foundation models on proprietary data, or training mid-scale models from scratch. Enough headroom for meaningful runs without paying for capacity you won't use.
Rendering & Creative AI
Image, video and 3D at production quality
For creative pipelines: 3D rendering, VFX, image and video generation. Tuned for parallel batch work where visual fidelity and throughput both matter.
Enterprise-Scale LLM
Foundation models at the largest scale
For training or serving foundation-scale models where interconnect, memory bandwidth and cluster throughput drive the design. Reserved capacity with the operational discipline to match.
Overview
One AI practice. Model to production.
We treat AI as an engineering discipline — data pipelines, model choice, evaluation, deployment and monitoring, all designed together.
Custom Model Development
Fine-tuning, adaptation and — where it's warranted — training from scratch on your proprietary data.
RAG & Retrieval Pipelines
Vector stores, chunking, re-ranking and grounding — retrieval built to survive real-world question shapes.
AI Copilots & Assistants
In-product copilots and domain-aware assistants engineered into your existing UX, not stapled onto it.
MLOps & Model Deployment
CI/CD for models, versioning, rollout controls and reproducible training — models managed like any other production system.
Enterprise AI Integration
Wire models into ERP, CRM, ticketing, data warehouses and identity — inside the same system of record your business already runs on.
Responsible AI & Evaluation
Evaluation harnesses, guardrails, bias and safety checks — the parts that decide whether a model can actually go live.
Outcomes
What an AI engagement is meant to change
We scope AI work against a decision or a process, not a demo. These are the shifts an engagement is designed to produce.
Decisions moved out of spreadsheets
Forecasting, scoring and prioritisation logic that currently lives in analyst workbooks becomes a governed model with versioning, monitoring and an audit trail.
Manual review reserved for edge cases
High-volume classification, extraction and triage run automatically, with confidence thresholds routing only ambiguous items to a human queue.
A model you can explain to a regulator
Feature lineage, training data provenance and evaluation results are documented so risk, audit and compliance teams can interrogate how an output was produced.
Data plumbing that survives the pilot
Pipelines, feature stores and retraining schedules are built as production infrastructure from the start, so the model does not decay once the project team leaves.
Cost per inference under control
Model choice, batching and caching are treated as engineering decisions, so unit economics are known before volume scales rather than discovered on an invoice.
Internal capability, not dependency
Your engineers and analysts are brought through the build so the system can be extended, retrained and debugged in-house after handover.
Specifically an LLM project?
This page covers the broader AI scope — AI features inside your software, intelligent workflows and decision systems. If the work is specifically fine-tuning, RAG or the infrastructure to serve a model in production, that's the deeper specialization.
See LLM Development & InfrastructureProcess
How an AI engagement unfolds.
Structured, measurable, and grounded in the data you actually have — not the data a slide deck assumed.
- 01
Discover
Use case, data landscape and success metrics defined before any model work starts.
- 02
Data & Architecture
Data pipelines, model selection and system architecture designed around the real workload.
- 03
Build & Train
Model development, fine-tuning or integration work, with evaluation baked in from day one.
- 04
Deploy & Monitor
Production deployment, MLOps pipelines and ongoing model performance monitoring.
Tech stack
The frameworks and tooling we work in.
A working stack chosen for what runs reliably in production — not what trends on this month's leaderboard.
Engagement model
How AI engagements are structured
Most AI work fails on scope, not on modelling. We start narrow, prove value against a defined evaluation set, then industrialise.
Discovery and feasibility sprint
A short, fixed-scope assessment of your data, the target decision and the realistic accuracy ceiling. Ends with an evaluation baseline, an architecture and a go/no-go recommendation — including when the honest answer is that rules or analytics would serve you better.
Best for: Teams with an idea and unproven data readiness.
Fixed-scope build
A defined use case taken from dataset to deployed, monitored service: pipelines, training, evaluation harness, serving layer and dashboards, with acceptance criteria agreed before work starts.
Best for: A validated use case with a clear success definition.
Embedded ML capability
Our engineers work inside your team on your board and your repositories, adding modelling and MLOps depth while your people retain ownership of direction.
Best for: Organisations building an internal data science function.
Managed model operations
Ongoing retraining, drift monitoring, incident response and cost tuning for models already in production, under an agreed response and reporting cadence.
Best for: Live models with no dedicated MLOps owner.
Industries served
AI applied where the stakes are real.
Model patterns hardened by the sectors that demand explainability, uptime and compliance.
- FinanceRBI/SEBI-regulated environment
- HealthcareDPDP & data-privacy focused
- GovernmentPublic-sector compliance
- ManufacturingOT/ICS-aware
- RetailPCI-DSS aware
- EducationStudent data-privacy focused
- HospitalityGuest-data & PCI-DSS aware
- StartupsBuilt for speed, not red tape
- EnterpriseAudit-ready, governance-first
FAQ
Questions we get asked before an AI project starts
Direct answers on scope, data handling, cost and ownership — the things that decide whether an AI programme is worth starting.
Let's build your AI system.
Tell us the workload. We'll bring the model, the pipeline, the compute — and the discipline to keep it working after launch.