Services
Seven capabilities. One goal: products that run.
Every service ships to the same standard: a clear problem definition, verifiable methods, visible deliverables.
System Development
Custom software platforms, from architecture to long-term operations.
The problem
Off-the-shelf software doesn't fit your workflow and the legacy system resists change. The business waits while tech debt grows.
Our approach
Audit workflows and data first, then design a modular architecture. Ship in short iterations — every stage deployable, reversible, measurable.
Deliverables
- Architecture documentation
- Modular code with tests
- CI/CD and deployment pipeline
- Operations runbook and monitoring
Use cases
Frontend
Interface engineering that wins on performance and detail.
The problem
Janky interfaces, slow loads, broken mobile layouts — users churn at first impression.
Our approach
Next.js/React at the core, driven by a design system. Core Web Vitals as KPIs; accessibility and responsiveness by default, not as add-ons.
Deliverables
- Design tokens and component library
- Responsive page implementation
- Performance and SEO reports
- Accessibility audit
Use cases
Backend & API
Scalable, observable, maintainable server-side architecture.
The problem
Falls over under traffic, failures are untraceable, and only one person understands the backend.
Our approach
Clear service boundaries and API contracts, full logging and monitoring, load-tested scaling strategies. Docs and handover are part of the deliverable.
Deliverables
- API design and docs (OpenAPI)
- Database schema and migrations
- Monitoring, alerting and logs
- Load test reports
Use cases
AI / Machine Learning
Complete ML pipelines — from data to model to production.
The problem
The model scores well in a notebook — then ships with no maintenance, no monitoring, and nobody willing to touch it.
Our approach
Treat ML as engineering: versioned data and models, automated training/eval pipelines, drift monitoring and retraining after launch.
Deliverables
- Data preprocessing pipelines
- Model training and evaluation reports
- Inference APIs and deployment
- Monitoring and retraining loops
Use cases
LLM Systems
LLM applications: RAG, agents, evaluation and deployment.
The problem
An LLM demo takes five minutes. A production system has to handle hallucination, cost, latency and evaluation — that's the hard part.
Our approach
Contain hallucination with retrieval and tool calls, guard quality with eval sets, control cost with caching and model tiering. Multi-model architecture avoids vendor lock-in.
Deliverables
- RAG / agent system implementation
- Eval sets and quality dashboards
- Cost and latency optimization
- Prompt and version-control workflow
Use cases
AI Database & Data Engineering
Turn data into an asset: pipelines, warehousing, vectors, governance.
The problem
Data scattered across ten systems in ten formats — nobody dares delete it, nobody dares use it. AI projects stall at step one.
Our approach
Automated ingestion and cleaning pipelines, a unified data model with quality checks; vector indexes and feature stores ready for AI workloads.
Deliverables
- ETL/ELT pipelines
- Data warehouse and models
- Vector database setup
- Data quality and governance standards
Use cases
AI Marketing Integration
Wire AI into marketing workflows, from insight to automation.
The problem
You have the marketing data and you bought the AI tools — but they don't connect, and insights die in slide decks.
Our approach
Connect data sources to the marketing stack; AI generates insights that trigger actions automatically — every step trackable, attributable, iterable.
Deliverables
- Data integration pipelines
- Automated marketing workflows
- Attribution dashboards
- Content-generation tooling