$ echo $IMPACT_METRICS
$ cat tech-stack.json
🤖 AI & Machine Learning
⚡ Core Technologies
🔧 Supporting Stack
☁️ Infrastructure & DevOps
$ cat README.md
Sparrow Intelligence is a systems-focused studio I founded to build AI-native backend platforms for engineering teams that demand reliability, clarity, and scale — not proof-of-concept demos that fall apart in production.
Our work typically begins where existing systems become hard to reason about: large codebases, fragmented documentation, and complex data flows that have outgrown their original architecture.
We specialize in transforming these challenges into well-structured, observable systems that engineering teams can confidently extend and maintain.
$ git log --oneline responsibilities/
$ grep -r "achievement" ./
$ cat CHALLENGES.md
Context Window Optimization for Large Codebases
Enterprise clients needed AI systems to reason about codebases with millions of lines of code, far exceeding any model's context window.
Designed a hierarchical code indexing system using AST parsing and semantic embeddings. Implemented intelligent context selection that surfaces only the most relevant code snippets, reducing token usage by 80% while maintaining answer quality.
Reliable Agentic Workflows at Scale
AI agents would hallucinate tool calls or enter infinite loops when handling complex multi-step tasks.
Built a robust agent orchestration layer with structured output validation, retry strategies, and circuit breakers. Implemented comprehensive observability with LangSmith for debugging and monitoring agent behavior in production.
Real-time Knowledge Sync Across Systems
Knowledge bases became stale within hours as source documents were updated across multiple systems (Confluence, Notion, GitHub).
Implemented event-driven ingestion pipelines with webhooks and polling fallbacks. Built incremental indexing that updates only changed documents, reducing sync time from hours to under 5 minutes.
$ cat details.md
Why I Started Sparrow Intelligence
After nearly a decade of building backend systems and witnessing the AI revolution firsthand at companies like Anaqua and Flowrite, I saw a clear gap in the market: most AI implementations fail not because of the AI, but because of poor engineering.
Teams were excited about LLMs but struggled with:
- Integrating AI into existing systems without disrupting workflows
- Building retrieval systems that actually return relevant results
- Creating agents that are reliable enough for production use
- Scaling AI workloads without costs spiraling out of control
Sparrow Intelligence exists to bridge this gap — bringing senior engineering rigor to AI implementation.
Our Technical Philosophy
Production-First Architecture
Every system we build starts with observability, monitoring, and graceful degradation. We design for the failure modes that demos never encounter.
Semantic-First Data Design
We treat embeddings and vector stores as first-class citizens in our data architecture, not afterthoughts. This enables powerful semantic search and retrieval from day one.
Composable AI Components
Rather than monolithic AI systems, we build composable components that can be independently tested, deployed, and scaled. This reduces risk and accelerates iteration.
Open to Collaboration
I’m selectively taking on B2B partnerships and long-term engineering collaborations with teams that:
- Have complex technical challenges that require senior expertise
- Value reliability and maintainability over speed-to-demo
- Are building products where AI is core to the value proposition
If this resonates, let’s talk: Book a Meeting
Related
Technologies: LangChain, RAG Systems, AI Agents, FastAPI, Python, OpenAI, Anthropic Claude, MCP
Similar Roles: AI Backend Lead at Anaqua | Senior Engineer at Flowrite