Most AI projects die in pilot.
The model works in a notebook and never reaches a user. We've shipped AI systems that operate in production — including the AI engine behind Microvest, which analyzes real-time Bitcoin market data and sentiment signals to power data-backed investment intelligence and surface personalized portfolio insights to users. We've also embedded LLMs directly into the Clust GPU cloud platform's production data pipelines for automated classification, structuring, and anomaly detection. RAG pipelines, AI agents, fine-tuning, and vector database integrations — engineered to deploy, monitor, and scale, with shipped track record across fintech and data infrastructure.
The patterns we see kill projects before they ship.
“Your AI demo wowed the board. It's been six months. It's still a demo.”
Pilot purgatory is structural, not technical. The work that takes a notebook to a production system — evaluation harnesses, prompt versioning, cost controls, fallback behaviour — is the work most teams skip and then can't recover from.
“Your LLM hallucinates citations. Your legal team isn't laughing.”
Hallucinations in regulated contexts aren't just embarrassing — they're liability. Solving them requires retrieval architecture, output validation, and human-in-the-loop patterns engineered with the same care as a financial system.
“Your RAG pipeline returns wrong results 30% of the time.”
Retrieval quality is almost always upstream of the model — chunking, embedding choice, hybrid search, re-ranking. Tuning the LLM rarely fixes a retrieval problem.
How we engage, scope, and ship.
Use-Case Triage
Distinguish what AI actually solves from what looks AI-shaped. Not every problem needs a model — some need a query.
System Design
Retrieval, model, evaluation, monitoring, and cost design — the full system, not just the prompt.
Production Build
Engineered like the rest of our infrastructure: testable, observable, deployable. Eval harnesses from day one.
Operate & Iterate
Monitoring, drift detection, cost dashboards, and ongoing prompt / model evolution as your product evolves.
The full stack for this lane — engineered to live in production.
RAG & Retrieval
- Document ingestion and chunking strategy
- Embedding model selection and benchmarking
- Hybrid search (vector + BM25) and re-ranking
- Vector databases: Pinecone, Qdrant, Weaviate, pgvector
- Multi-tenant retrieval with permission-aware search
LLM Engineering
- Fine-tuning (LoRA, QLoRA, full fine-tune)
- Prompt engineering and versioning
- Function-calling and structured output
- Multi-model routing and fallback
- Self-hosted and privacy-first deployments
AI Agents & Autonomous Systems
- Agent frameworks (LangGraph, CrewAI, custom)
- Tool-use and function-calling integration
- Memory architecture (short-term, long-term, episodic)
- Multi-agent coordination patterns
- Production-safe autonomous decision systems
MLOps & Evaluation
- Eval harness design (LLM-as-judge, human eval, golden datasets)
- Cost monitoring and per-feature attribution
- Drift detection and re-training triggers
- A/B testing for prompts and models
- Output safety, content filtering, hallucination guards
AI systems shipped in production fintech and data infrastructure.
Microvest — AI Bitcoin investment platform. Production engineering for the AI engine that analyzes real-time Bitcoin market data and sentiment signals to power data-backed investment intelligence and personalized portfolio insights. Paired with full custodian management and BTC transaction infrastructure.
Clust GPU cloud platform. Production LLM-embedded data pipelines — automated classification, semantic enrichment, structuring, and anomaly detection at platform volume.
We apply the same engineering discipline to AI that powers our blockchain and cloud work. The difference between a notebook prototype and a production AI system is operational rigour — eval harnesses, monitoring, fallback behaviour, and cost controls — and that's where most teams stall.
- Microvest AI engine — real-time Bitcoin market data and sentiment analysis, data-backed investment intelligence, personalized portfolio insights for users.
- Clust LLM-embedded pipelines — production classification, semantic enrichment, structuring, and anomaly detection at platform volume.
- Privacy-first AI architectures available for clients with strong legal and infrastructure boundaries.
- Eval harnesses and cost monitoring built in from day one — not bolted on after launch.
- Same operational discipline that runs production blockchain and data infrastructure — applied to AI.
Three ways to bring AlgoCoder into your build.
AI Strategy Call
Single-session strategy engagement to triage your AI ideas and recommend what's worth building. Best as a first conversation.
Pilot-to-Production
Take an existing notebook prototype to a production-grade system. Scoped, priced, delivered with eval harness and monitoring built in.
Dedicated AI Team
Senior AI engineers and ML practitioners focused on ongoing platform AI work over multiple quarters.