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Full-spectrum capability

A full capability stack.
Four ways we create value.

Twelve capabilities across build, run, extend and scale. Engage one of them or all of them — the accountability line stays in the same place.

The capability stack

Twelve capabilities. One accountable partner.

Most buyers arrive needing two or three of these. The ones that follow are usually the reason the first three stalled somewhere else.

Build

Get the first production capability shipped.

AI-native software development

Products designed around models and retrieval from the first commit, not retrofitted later.

AI implementation & consulting

Readiness audit, use-case triage, and a roadmap scoped to ROI rather than activity.

Data engineering & modernisation

The pipelines, contracts and quality gates that make your proprietary data usable.

Run

Keep it fast, safe and observable in production.

MLOps & LLMOps managed services

Pipelines, registries, drift and cost monitoring, automated retraining triggers.

Quality engineering & testing

Full testing pyramid, plus hallucination, bias and red-team evaluation for generative systems.

Governance, security & compliance

Access control, audit trails and model risk controls designed in, not bolted on.

Extend

Push into higher-value AI surfaces.

Generative & agentic AI

Multi-step agents grounded in your data, with evaluation harnesses in place before rollout.

Conversational AI & assistants

Chat and voice interfaces wired into the systems that actually hold the answers.

Vertical & industry AI

Domain-shaped solutions for telecom, banking, semiconductor and healthcare workflows.

Scale

Turn one delivery into an operating capability.

AI Center of Excellence

Standards, reusable platform components and the governance forum to enforce both.

Staffing-as-a-Service & GCC setup

Embedded specialists in India, or a full build-operate-transfer centre with your name on it.

Startup Growth Partner Program

A right-sized build, test and ops bench priced against your stage and your burn.

Reference architecture

How we build it: a layered, AI-native architecture.

Four layers. We design, run and staff every one of them — which is what stops the hand-off failures that kill most AI programmes.

ExperienceWhat your people and customers actually touch

Copilots & agentsChat & voice interfacesDashboards & analyticsWorkflow embedding

OrchestrationWhere reasoning is coordinated and measured

Agent orchestrationRAG pipelinesPrompt & eval harnessesGuardrails

MLOps & dataThe machinery that keeps models honest

Feature storesVector databasesModel & prompt registriesPipelines & monitoring

InfrastructureThe floor everything else stands on

Cloud & KubernetesIAM & securityCompliance controlsCost governance

Where we go deepest

Four disciplines, done all the way through.

These are the engagements we are known for, and the ones we will happily be judged on.

Build

AI-native development & implementation

  • Readiness and opportunity audit before a single line of code — we scope for ROI, not activity.
  • RAG and agentic architectures grounded in your proprietary data, not a generic LLM wrapper.
  • Reference architectures spanning data ingestion, retrieval, orchestration and application layers.
  • Production engineering discipline from day one: version control, code review, staged environments, CI/CD.
  • From proof-of-concept to production, with a named owner for every milestone.
Run

MLOps, LLMOps & quality engineering

  • CI/CD pipelines purpose-built for ML: automated training, validation and promotion gates.
  • Model and prompt registries, feature stores and vector databases as first-class infrastructure.
  • Real-time drift, latency and cost monitoring with automated retraining triggers.
  • Full testing pyramid — unit through performance and security — plus hallucination, bias and red-team evaluation.
  • Canary and shadow deployment patterns, so nothing reaches every user untested.
Scale

Staffing-as-a-Service & GCC setup — India

  • Embedded ML engineers, data scientists, QA and DevOps specialists — sourced and vetted, never subcontracted.
  • End-to-end GCC build-operate-transfer: entity guidance, facility, tooling, hiring velocity and governance.
  • Structured onboarding and knowledge-transfer protocols built in from week one, not bolted on at exit.
  • Flexible scaling from one engineer to a two-hundred-person centre, on one delivery backbone.
Scale

Startup Growth Partner Program

  • Right-sized pods — from a single senior engineer to a full build, test and ops team.
  • Senior technical talent at startup-friendly economics, without the overhead of direct hiring.
  • Technical due-diligence readiness: architecture, security and testing hygiene ahead of your next raise.
  • Modernisation and scale-readiness, so growth doesn't outpace your infrastructure.

Technology fluency

Platform-agnostic. Built in the stack you already trust.

We have no reseller relationships to defend, so the recommendation you get is the one we would make for ourselves.

Cloud & infrastructure

  • AWS
  • Microsoft Azure
  • Google Cloud
  • Kubernetes
  • Terraform

Foundation models & APIs

  • OpenAI
  • Anthropic
  • Google Gemini
  • Llama
  • Mistral

Agent & orchestration

  • LangChain / LangGraph
  • LlamaIndex
  • Vector databases
  • RAG pipelines

MLOps & data platforms

  • MLflow
  • Kubeflow
  • Databricks
  • Airflow
  • Feature stores

Testing & quality

  • Selenium
  • Playwright
  • k6 (performance)
  • Postman (API)

Observability & governance

  • Grafana
  • Prometheus
  • Datadog
  • Evaluation harnesses

Ready when you are

Tell us where you're stuck in the stack.

Bring us the layer that isn't working. We'll tell you honestly whether it's an architecture problem, an operations problem, or a people problem.

Book a readiness call See how we deliver