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Service Line — AI & Data Intelligence

AI that moves
from boardroom
to production.

We build enterprise AI systems that don’t live in Jupyter notebooks. From data architecture and feature engineering to deployed, observable models — creating compounding value at every layer of your stack.

Start with a proof of value → Explore capabilities

// What we build

Full-spectrum intelligence engineering.

Generative AI & LLMs
MLOps & Platforms
Data Architecture
Enterprise RAG Systems
Retrieval-Augmented Generation that gives your LLMs accurate, cited, real-time access to proprietary knowledge — without hallucination.
LangChainLlamaIndexPineconeWeaviate
LLM Fine-tuning & Alignment
Domain-specific model fine-tuning using RLHF, LoRA, and QLoRA — giving enterprise models that understand your industry's vocabulary.
GPT-4ClaudeLlama 3Mistral
AI Agents & Orchestration
Multi-agent systems that reason, plan, and execute complex workflows autonomously — integrating with your existing enterprise systems.
AutoGenCrewAILangGraph
Multimodal AI
Vision, audio, and document understanding at enterprise scale — automating document processing, quality inspection workflows.
GPT-4oClaude VisionGemini
ML Platform Engineering
End-to-end ML platforms covering experiment tracking, model registry, feature stores, and automated retraining pipelines at production scale.
MLflowKubeflowSageMakerVertex AI
Model Monitoring & Drift Detection
Production model observability with automated drift detection, data quality gates, and performance degradation alerting.
Evidently AIArizeWhyLabs
Feature Store Architecture
Centralized feature management that eliminates training-serving skew, enables feature reuse, and reduces model development time by 60%.
FeastTectonHopsworks
Model Serving & Inference
High-throughput, low-latency model serving with GPU optimization, batching strategies, and auto-scaling for enterprise traffic patterns.
TritonTorchServeBentoML
Lakehouse Architecture
Unified storage and compute layers that serve both analytics and ML workloads — eliminating data silos and enabling governed, reliable data access.
Delta LakeApache IcebergDatabricks
Real-time Streaming Pipelines
Event-driven architectures that process millions of events per second with sub-100ms latency — enabling real-time personalisation and fraud detection.
Apache KafkaFlinkKinesis
Data Mesh Implementation
Domain-oriented data ownership and architecture that scales governance without creating central bottlenecks.
dbtApache AtlasDatahub
Cloud Data Warehousing
Modern cloud DWH implementation with cost optimisation, performance tuning, and automated data quality at petabyte scale.
SnowflakeBigQueryRedshift

// Technology Radar — Q1 2025

What we’re betting on,
watching, and stepping back from.

Adopt Adopt
RAG with hybrid search
LLM fine-tuning with LoRA
Feature stores (Feast/Tecton)
Delta Lake / Iceberg
dbt for data transformation
ML model monitoring
Trial Trial
Multi-agent frameworks (CrewAI)
GraphRAG
Speculative decoding
Mixture of Experts (MoE)
Structured outputs (JSON mode)
AI-powered data quality
Assess Assess
Synthetic data generation
Neuromorphic computing
Quantum ML
Embodied AI agents
On-device LLM inference
AI memory systems
Hold Hold
Pure Spark for batch ML
Legacy feature engineering pipelines
Monolithic ML notebooks
Single-tenant ML platforms
Heavy custom NLP (pre-LLM)
Batch-only model scoring

// Featured Work

BFSI · Real-time AI · 2024

Real-time fraud detection platform for a Tier-1 bank processing 12M daily transactions

A leading Indian bank needed to replace their rule-based fraud detection system generating 40% false positives. We built an ML-powered real-time scoring engine integrated directly into their payments infrastructure — deployed at 8ms p99 latency with zero-downtime model updates.

0%
Detection accuracy
0ms
p99 latency
0%
Fewer false positives
$12M
Annual fraud prevented
Read full case study →

Technologies we work with

PythonPyTorchTensorFlowscikit-learnHugging FaceLangChainLlamaIndexApache KafkaApache FlinkApache SparkdbtDatabricksSnowflakeBigQueryRedshiftDelta LakeMLflowKubeflowSageMakerVertex AIFeastPineconeWeaviateKubernetesDockerTerraformAirflow

Start with a
proof of value.

Every engagement begins with a 2-week proof-of-value sprint. You get a working model, a commercial case, and a production roadmap — before committing to a full programme.

Book your proof of value → How we engage