Seven practices. One delivery standard.
We work across the stack — models, product, data, and the strategy that decides which of those you actually need. Every engagement ends in something you can act on: working software you own, or a strategy with the numbers behind it — and a measurement that says whether it did the thing we agreed it would do.
Applied AI & LLM Systems
Retrieval, agents, and copilots built for a specific workflow, with an evaluation harness so you can tell whether the thing actually works before it reaches a user.
Representative stack
- Claude
- OpenAI
- LangGraph
- pgvector
- FastAPI
What you get
- Working prototype against your data
- Evaluation suite with a measured baseline
- Latency and unit-cost model
- Failure-mode write-up
AI Research → Product
We read the papers anyway. This is the translation layer: which recent result is load-bearing for your problem, what it costs to implement, and where it quietly fails.
Representative stack
- PyTorch
- HuggingFace
- Weights & Biases
- CUDA
What you get
- Literature-to-roadmap memo
- Reproduction of the key result
- Build-or-wait recommendation
Full-Stack Engineering
Prototype to production without a rewrite in between. Typed end to end, deployed on infrastructure your team can take over the day we leave.
Representative stack
- TypeScript
- Next.js
- Postgres
- Docker
- Vercel
- AWS
What you get
- Production-deployed application
- CI pipeline and test coverage
- Handover documentation
Short engagements, written down
We are students, and we scope like it. Work is sized to finish inside a semester, staffed small enough that nobody is a spectator, and pinned to a success metric we agree on in the first week.
Four checkpoints on fixed dates. Long enough to ship something real, short enough to finish inside one semester.
One lead owns the relationship. Everyone on the team has commits in your repository.
One sentence with a number in it, agreed before we build. It is what we are judged on at the end.
What you are buying is speed, technical depth, and a direct line into Berkeley research. Not a bench you can draw against for a year. If a problem needs more than twelve weeks, we scope the first twelve honestly and tell you what is left. If we are the wrong team for it, you hear that on the first call rather than in week six.
Data & Platform Engineering
The unglamorous prerequisite. Instrument data lands somewhere queryable, schemas are documented, and the pipeline does not need a person watching it.
Representative stack
- Python
- dbt
- Postgres
- Airflow
- Snowflake
What you get
- Ingestion pipeline with monitoring
- Documented schema and lineage
- Backfill and replay tooling
Technology Strategy & Diligence
Consulting rigour without the abstraction. Competitive teardowns, technical diligence, and build-versus-buy calls written by people who have built the thing being evaluated.
Representative stack
- Patent analysis
- Market sizing
- Architecture review
What you get
- Competitive capability matrix
- Technical diligence report
- Decision memo with a recommendation
Business & Market Strategy
Market entry, go-to-market, pricing, and growth. Half of our leads are business majors in Berkeley's M.E.T. program, and this is the half of the practice where the deliverable is a decision: which segment, at what price, against whom, and what the numbers have to look like for it to be worth doing.
Representative stack
- Market sizing
- Customer interviews
- Unit economics
- Competitive positioning
What you get
- Market-entry or GTM strategy memo
- Segmentation and pricing analysis
- Customer-discovery synthesis from real interviews
- Unit-economics model you can rerun yourself
Lab & Scientific Software
Where our bench experience pays for itself: instrument data, regulated workflows, and the difference between software a scientist tolerates and software they open on purpose.
Representative stack
- LIMS/LES
- Chromatography data
- HL7/FHIR
- Python
What you get
- Workflow map with named bottlenecks
- Protocol-fidelity evaluation
- Instrument-data integration prototype
Tell us the problem. We will tell you if we can ship it.
One call is usually enough to know whether this is a fit, which of the seven areas it lands in, and what the first twelve weeks would look like.