What we build
We solve problems.
These are the tools.
The capability is commoditised — everyone has the same models. What matters is knowing which problem to point them at. Here’s what we build once we’ve diagnosed yours.
AI-Native
Production AI — agentic, retrieval-augmented, and real.
Systems designed for AI from the ground up — with evaluation frameworks, observability, and production reliability built in from day one.
Agentic Workflows
Multi-step AI agents that plan, use tools, and execute tasks. Architecture, tool schemas, memory, and fallback logic — not just prompts.
We build
Orchestration and planning layers
Tool use, function calling, API integrations
Eval harnesses for agent reliability
RAG Systems
Production retrieval-augmented generation over enterprise data. Hybrid search, re-ranking, latency-engineered pipelines — not naive RAG.
We build
Ingestion pipelines and chunking strategy
pgvector, Pinecone, Weaviate
Latency and accuracy benchmarking
AI Copilots
Intelligent assistants inside your existing product. Domain-aware, data-aware, continuously improving through evals.
We build
Domain-specific prompting and fine-tuning
Streaming UI with citation and reasoning
Feedback loops and output guardrails
0→1 AI Products
Full products built AI-first from day one. Architecture decisions that don’t box you in on day 90. MVP to first revenue in 8–16 weeks.
We build
Product scope and AI architecture design
Full-stack: frontend, backend, AI layer
Model selection and provider strategy
Product Engineering
Full-stack engineering — from first commit to enterprise scale.
SaaS Product Builds
End-to-end SaaS — multi-tenant architecture, auth, billing, dashboards, APIs. Built for growth from day one.
Stack
Next.js, React, React Native
Node.js, Python, Go, PostgreSQL
Stripe, Auth0, multi-tenancy patterns
Scale Engineering
Post-PMF to enterprise-grade. Architecture hardening, database performance, reliability — without rebuilding from scratch.
We address
Database performance and query optimisation
Caching layer architecture
CI/CD, IaC, deployment reliability
Embedded Engineering Pods
2–4 senior engineers embedded in your team. Full delivery ownership. Clear exit criteria. No permanent overhead.
How it works
Works inside your sprint and tools
Fixed scope with clear exit criteria
Knowledge transfer built in
Engineering Standards
How we build —
not just what.
Every system Flipr ships is built to standards that enterprise procurement can audit. Practice, not aspiration.
Architecture
Documented ADRs, scalable data models. Systems your team owns after handoff.
Architecture Decision Records
System design review
Infrastructure as code
Security
Secure-by-default. Data handling your security team approves on first review.
Encryption at rest and in transit
RBAC and audit logging
Secrets management
Reliability
SLO definition, incident runbooks, monitoring from launch day.
SLO/SLA definition
Graceful degradation patterns
Load testing and capacity planning
Observability
Full distributed tracing. Systems that alert before your users notice.
OpenTelemetry tracing
Structured logging + correlation IDs
Metric dashboards and alerting
AI Evals
Eval harnesses for LLM systems. Hallucination detection, output scoring, regression suites.
Eval dataset construction
Faithfulness and relevance scoring
Latency and cost benchmarking
Start building