๐Ÿค–
Generative AI

LLM Apps, RAG & Agents

We design retrieval-augmented assistants, multi-agent workflows and domain copilots โ€” with evals, guardrails and observability baked in.

  • Knowledge base & vector search (Qdrant, pgvector, Azure AI Search)
  • Agentic workflows (LangGraph, custom orchestrators)
  • Fine-tuning, prompt engineering, evals
๐Ÿ“Š
Big Data

Data Platforms & Lakehouses

We modernise legacy warehouses and build cloud-native lakehouses that scale โ€” without breaking budgets.

  • Databricks, Snowflake, Synapse, BigQuery
  • Delta / Iceberg lakehouse architectures
  • Batch + streaming ELT (Spark, dbt, Airflow)
๐Ÿง 
Data Science

ML Modeling & MLOps

From feature stores to deployed inference โ€” we ship models that hold up in production with continuous evaluation.

  • Forecasting, classification, recommendation
  • MLflow, model registries, drift monitoring
  • Feature stores, batch & real-time scoring
โšก
Real-time

Streaming & Event Systems

Sub-second pipelines for fraud detection, IoT telemetry, observability and personalisation.

  • Kafka, Flink, Kinesis, Event Hubs
  • ClickHouse, Druid for real-time analytics
  • CDC pipelines, Debezium, exactly-once semantics
๐Ÿ”
BI & Analytics

Decision Intelligence

Executive dashboards, embedded analytics and self-serve BI that drive the metric that matters.

  • Power BI, Tableau, Superset, Metabase
  • Semantic layer & metric stores
  • Embedded analytics for SaaS products
๐Ÿ›ก๏ธ
Cloud / Sec

Cloud Engineering & DevOps

Cloud-native foundations on Azure & AWS with zero-trust, IaC and cost guardrails โ€” built to scale and audit.

  • Terraform, Bicep, Pulumi
  • Kubernetes, AKS, EKS, serverless
  • SOC2-ready logging, IAM, FinOps
Technology

A modern stack, deeply understood.

We don't chase fashion โ€” we pick what survives production.

Python
C# / .NET
TypeScript
PyTorch
TensorFlow
LangChain
Spark
Kafka
Flink
Databricks
Snowflake
ClickHouse
SQL Server
PostgreSQL
MongoDB
Azure
AWS
Kubernetes
Terraform
Airflow
dbt
Power BI
React
Next.js
Engagement Models

Work with us, your way.

Fast-track

2-Week AI Sprint

Working POC for one well-defined use case โ€” model, UI, evals, and a go/no-go report.

Build

Project Delivery

Fixed-scope or milestone-based engagements with weekly demos and a production deployment.

Ongoing

Dedicated Pod

A cross-functional pod โ€” engineers + ML + design โ€” embedded with your team for 3โ€“12 months.

ML analytics
ML Engineering

Models that survive launch

We've watched too many ML projects die in pilot. Our delivery model is built around the unglamorous parts that decide whether a model lives or dies in production.

  • Feature stores with online + offline parity
  • MLflow / registry with versioned weights and rollback
  • Drift detectors, shadow deployments, A/B harness
  • Inference tuned for cost, latency and accuracy together
Engineering excellence

Code is the first artifact, not the only one.

Every engagement ships architecture docs, ADRs, eval harnesses, observability, CI/CD, IaC and a runbook โ€” because production isn't a finish line, it's a starting point.

Start a Project โ†’
Data infrastructure
Data Engineering

Pipelines that don't page you at 3am

Idempotent, observable, replayable โ€” the boring properties that quietly make the difference between calm operations and weekend incidents.

  • Exactly-once semantics with Kafka, Flink and Debezium CDC
  • Schema registries, contract tests and breaking-change alerts
  • Backfill paths and replay tooling built in from day one
  • Cost-aware data tiering โ€” hot / warm / cold automation
Deliverables

What you actually get

Every engagement ships these โ€” not just slides and a slack channel.

๐Ÿ“

Architecture docs

System diagrams, ADRs and trade-off notes you can hand to any future engineer.

๐Ÿงช

Eval harness

Reproducible evaluation suite that scores models and pipelines on every change.

๐Ÿ“Š

Observability

Logs, traces and dashboards wired into your stack from day one โ€” not an afterthought.

๐Ÿš€

CI/CD pipelines

One-click deploys, IaC, rollback paths and pre-prod environments.

๐Ÿ“˜

Runbooks

Incident playbooks, on-call rotations and recovery procedures.

๐ŸŽ“

Team enablement

Pair-programming, code-walkthrough sessions and recorded knowledge transfer.

๐Ÿ”

Security review

Threat model, IAM audit and a hardening checklist signed off before go-live.

๐Ÿ“ˆ

KPI report

Final outcome memo measuring the business metric we agreed on at kickoff.

Industries

Domains we know cold

๐Ÿฆ
BFSI
Fraud ยท risk ยท CX
๐Ÿ›’
Retail / D2C
Personalisation
๐Ÿฅ
Healthcare
Clinical AI
๐ŸŽ“
EdTech
Adaptive learning
๐Ÿจ
Hospitality
Revenue mgmt
๐Ÿ—ณ๏ธ
Govt analytics
Election ยท census
FAQ

Common questions

How do you price engagements?

Three models: fixed-price for well-scoped projects, milestone-based for medium-complexity builds, and monthly retainers for dedicated pods. We share a written estimate after a 30-minute scoping call.

Do you work with NDAs and enterprise procurement?

Yes. We routinely sign mutual NDAs, MSAs, DPAs and pass through procurement security reviews for clients in BFSI, healthcare and government adjacent sectors.

Who owns the IP and the code?

You do. All deliverables โ€” source, models, weights where applicable, infra-as-code and documentation โ€” are transferred to your repositories and accounts.

Can you take over an existing project that's stalled?

Often yes. We start with a 1-week audit: codebase review, architecture assessment, dependency check, and a written take-over plan with risks and timelines.

How do you handle data privacy and residency?

We work inside your cloud accounts whenever possible, use synthetic / masked data for development, and respect data-residency constraints (India, EU, US). Logging, IAM and audit trails are designed in from day one.

What's your typical team composition?

A pod typically has 1 tech lead, 2โ€“4 engineers (mix of backend, ML, frontend as needed), and fractional design + product input. You'll know each engineer by name and meet them weekly.

How fast can you start?

For a 2-week AI sprint, usually within 7โ€“10 days of signoff. For a full pod engagement, 3โ€“4 weeks depending on the seniority mix.

Not sure which fits? Let's scope it together.

A 30-minute call is usually enough to know.

Book a call โ†’