Information Retrieval & Search
Hybrid retrieval done well — the boring wins that outperform embedding-only stacks.
- Hybrid dense + sparse retrieval
- Learning-to-rank models
- Query understanding & rewriting
- Multilingual search
Services
Engagements that ship.
Six practices, all in service of measurable relevance. Engagements typically run 4–16 weeks with clear success criteria set on week one — and honest reporting on the outcome.
Retrieval-Augmented Generation (RAG)
RAG that ships — grounded, evaluated, and honest about what it doesn’t know.
- Chunking & indexing strategy
- Grounded citations & guardrails
- Evaluation harnesses
- Cost & latency optimization
Relevance Engineering
A number for every change. Before ship, after ship, and every day in between.
- Judgment list creation
- Offline metrics (nDCG, MRR, MAP)
- Online A/B & interleaving
- Continuous eval pipelines
AI Strategy
Decisions grounded in evidence, not vendor slide decks.
- Model & vendor selection
- Build-vs-buy analysis
- Roadmaps & team design
- Technical due diligence
Vector Search & Embeddings
When embeddings help, when they hurt, and how to scale them in production.
- Vector store selection & tuning
- Embedding model evaluation
- Multimodal & ColPali retrieval
- Cost & recall tuning
NLP
Domain-specific NLP that earns its keep — no fine-tuning theatre.
- Extraction & classification
- Domain-adapted embeddings
- Fine-tuning & distillation
- Evaluation frameworks
Discuss a project
Not sure which of these you need?
Send us a two-line description of what you’re building and we’ll reply with an honest read: which service fits, or which doesn’t.