AI that ships to production
Most AI never leaves the demo. We engineer intelligent systems that are reliable, observable and cost-aware — the kind you can actually put in front of customers.
The full AI stack
From the data layer to the interface — everything intelligent, under one roof.
Generative AI & LLMs
Copilots, chat, summarization and content systems on the latest foundation models.
RAG & Knowledge
Retrieval-augmented generation over your data — accurate, cited, and grounded.
Autonomous Agents
Multi-agent systems that plan, use tools and act — with humans in the loop.
Custom Models
Fine-tuning and bespoke models for classification, prediction and ranking.
Computer Vision
Detection, OCR, quality inspection and video understanding at the edge.
Data & Vectors
Pipelines, feature stores and vector databases that keep models fed and fast.
Evals & Guardrails
Automated evaluation, red-teaming and safety rails before anything ships.
MLOps & Scale
Deployment, monitoring and cost optimization for inference in production.
Domain knowledge before model choice
The hard part of AI isn't the model — it's understanding the problem well enough to know what ‘correct’ means. We start by mapping your data, workflows and failure modes, then choose the smallest, most reliable approach that clears the bar.
Retrieval before fine-tuning. Evals before scale. Observability before launch. It's a methodology built to get you into production — and keep you there.
Most AI stops at the demo. We take it to production.
A model that dazzles in a sandbox is the easy 20%. The other 80% — reliability, evaluation, observability and cost — is where projects die, and where we do our best work.
- Impresses in the meeting, breaks on real inputs
- Hallucinates with no way to catch it
- No evaluation — quality is a vibe, not a number
- Zero observability once it's live
- Costs spiral with no one watching inference spend
- Reliable on the messy, real-world edge cases
- Grounded answers with guardrails and refusal
- Evals gate every release against a baseline
- Full observability — drift, latency and quality tracked
- Cost-aware inference, monitored and optimized
Our AI principles
Four non-negotiables that turn frontier models into systems you can put in front of customers.
Reliability first
Intelligent systems still have to be systems — deterministic where they can be, gracefully degrading where they can't.
Evals as gates
Automated evaluation sets run on every change. Nothing ships if quality regresses against the baseline.
Guardrails built in
Input validation, output filtering, red-teaming and safety rails — designed in from day one, not bolted on later.
Human in the loop
Agents propose, people approve. We design the escalation paths and controls that keep humans in command.
Our modernised AI & data strategy
A full-stack, cloud-agnostic architecture powering intelligent applications from model to production — every layer, under one roof.
- Data Encryption
- IAM & RBAC
- Key Management
- Audit Trails
- CI/CD Pipelines
- Model Registry
- A/B Testing
- Canary Deploys
- Real-Time Monitoring
- Cost Analytics
- Drift Detection
- Alerting
- Resource Management
- Scaling Policies
- DevOps Automation
- SLA Management
Enterprise-grade architecture built for limitless scale
We design systems that grow with your business — multi-tenant platforms, distributed microservices and AI-powered data pipelines engineered for reliability and performance.
Scalable Multi-Tenant Architecture
- Tenant-level data isolation on shared infrastructure for cost efficiency
- Dynamic resource allocation and auto-scaling per tenant workload
- White-label frontends with per-tenant configuration and theming
- Row-level security, schema- and database-per-tenant strategies
Microservices & Enterprise Architecture
- Domain-driven microservices with event-driven messaging (Kafka, RabbitMQ)
- API gateway with rate limiting, auth and service mesh (Istio / Envoy)
- CI/CD pipelines with containerized deploys on Kubernetes (EKS, GKE)
- Distributed tracing, centralized logging and real-time observability
AI Architecture & Data Pipelines
- End-to-end ML pipelines: ingestion, feature engineering, model serving
- Real-time streaming with Apache Spark, Flink and managed Kafka
- Vector databases and RAG architecture for LLM-powered apps
- Data lake / warehouse integration (Snowflake, BigQuery, Redshift)
From raw data to autonomous action
We own the whole intelligence stack — pipelines, models, agents and the interfaces that put them to work. No black boxes, no hand-offs.
Have an AI idea? Let's pressure-test it.
Our 2-week AI Discovery sprint maps feasibility, data readiness and ROI before you commit to a build.