Skip to content
Lankavia

02Technology

Infrastructure
for intelligence.

We work across the full path: contracts at the edge, governed data in the middle, and models that are versioned, evaluated and observable in production.

01Disciplines

Five disciplines,
one delivery model.

Teams are organised around outcomes rather than layers, so a single squad can take a problem from ingestion to inference.

01

Intelligence

  • Machine Learning
  • Generative AI
  • RAG
  • Agentic Systems
  • Computer Vision
  • NLP
02

Data

  • Data Engineering
  • Real-Time Pipelines
  • Data Warehousing
  • Feature Stores
  • Analytics
03

Infrastructure

  • Cloud
  • Kubernetes
  • MLOps
  • Observability
  • Distributed Systems
04

Web

  • Web Applications
  • Design Systems
  • Accessibility
  • Performance
  • Edge Delivery
  • E-commerce
05

Mobile

  • iOS
  • Android
  • Cross-Platform
  • Offline-First
  • Real-Time Sync
  • App Store Delivery

Operating surface

01

AI

Models applied to problems with a measurable definition of done.

02

Data

Pipelines with contracts, lineage and published grain.

03

ML

Training, evaluation and promotion treated as one workflow.

04

Cloud

Infrastructure defined as code, reproducible per environment.

05

RAG

Hybrid retrieval, re-ranking and grounded generation.

06

MLOps

Versioned models, shadow deploys and rollback by default.

07

Agents

Planners with tools, budgets and observable execution traces.

08

Automation

Deterministic workflows where randomness would be a liability.

03Pipeline

From data
to intelligence

Six stages, each with its own contract. Nothing moves forward until the previous stage is observable.

  1. 01

    Data

    Sources, contracts, events

  2. 02

    Ingest

    Stream and batch capture

  3. 03

    Process

    Validate, conform, enrich

  4. 04

    Model

    Train, evaluate, version

  5. 05

    Intelligence

    Score, retrieve, reason

  6. 06

    Action

    Decisions in production systems

05Tooling

Tools we reach for

A short list, kept deliberately boring. Tooling changes when the problem demands it, not when the trend does.

Languages

  • TypeScript
  • Python
  • Go
  • SQL

Data

  • PostgreSQL
  • Kafka
  • Airflow
  • dbt
  • Object storage

Machine learning

  • PyTorch
  • Feature stores
  • Vector search
  • Evaluation harnesses

Platform

  • Kubernetes
  • Terraform
  • OpenTelemetry
  • GitOps

Product

  • React
  • React Native
  • Swift
  • Kotlin
  • Design systems
01

Boring where it matters

Storage, queues and orchestration are chosen for operability. Novelty is reserved for the part of the system that creates the advantage.

02

Reversible decisions

Interfaces are treated as contracts so that a model, a vendor or a runtime can be replaced without rewriting the system around it.

03

Measured, not assumed

Every claim about latency, cost or quality is backed by a benchmark that runs in CI and fails the build when it regresses.

Next

Build
what comes
next.

Have a difficult technical problem? Let's build the system behind it.

Start a conversation