The kinds of problems we take to production.
A look at representative engagements across our practices. Client names are held in confidence — we're happy to walk you through the details on a call.
A retrieval assistant that experts actually trust
Built a grounded question-answering system over a large, messy internal knowledge base — with an evaluation harness that pushed answer accuracy past the bar domain experts set before rollout.
- ▸ Hybrid retrieval + reranking pipeline
- ▸ Citations on every answer, measured for faithfulness
- ▸ Continuous eval to catch regressions before users do
Automating a workflow no one wanted to do by hand
Designed a reliable agentic workflow to take a labor-intensive back-office process from hours to minutes — with guardrails and human-in-the-loop checkpoints where the stakes demanded them.
- ▸ Deterministic control flow around the model
- ▸ Full audit trail of every decision
- ▸ Graceful fallback when confidence is low
A roadmap that separated signal from hype
Ran an AI opportunity assessment for a leadership team facing a dozen competing ideas — returning a sequenced roadmap, honest effort estimates, and a clear first bet worth funding.
- ▸ Value-vs-feasibility scoring across use cases
- ▸ Build-vs-buy guidance per initiative
- ▸ A costed proof-of-value plan for the top bet
Making model quality measurable, not anecdotal
Built a reproducible evaluation framework so a team could compare models and prompt strategies on their own data — replacing gut feel with numbers they could defend to stakeholders.
- ▸ Task-specific benchmark on real examples
- ▸ Automated + human-in-the-loop scoring
- ▸ Clear cost / quality / latency trade-off view
Catching defects a human eye would miss
Built a computer-vision inspection model that flags defects from production-line imagery in real time — trained with limited labelled data using transfer learning, and calibrated to the false-positive tolerance the operations team needed.
- ▸ Detection & segmentation on high-resolution images
- ▸ Transfer learning to work with few labels
- ▸ Tuned decision threshold to the real cost of errors
A predictive model the business could act on
Developed a classical machine-learning model for demand forecasting and churn prediction on tabular data — interpretable, well-calibrated, and shipped with the monitoring to catch drift before it quietly degraded decisions.
- ▸ Feature engineering on real operational data
- ▸ Interpretable, explainable predictions
- ▸ Drift & performance monitoring in production
From scattered pilots to an AI-native way of working
Partnered with an established organization to turn stalled AI experiments into an operating reality — modernizing the data foundation, automating a core process end to end, and enabling the team to own and extend it after go-live.
- ▸ Data & platform modernization as the foundation
- ▸ Core workflow automation, not edge tooling
- ▸ Enablement so the change outlasted the project
A build-vs-buy call, settled with evidence
Brought into a stalled leadership debate, we ran a structured evaluation of the options — cost, risk, capability, and time-to-value — and delivered a clear, defensible recommendation. The deliverable wasn't a system; it was a decision the board could stand behind.
- ▸ Options scored against weighted, agreed criteria
- ▸ Total cost, risk & time-to-value modeled, not guessed
- ▸ A recommendation leadership acted on in weeks
An AI operating model the C-suite could commit to
Worked with executive leadership to move from scattered enthusiasm to a funded plan — defining where AI would create value, who would own it, how decisions would be made, and how success would be measured. We shaped the strategy; their teams owned it.
- ▸ Prioritized, costed portfolio of AI initiatives
- ▸ Operating model, ownership & decision rights
- ▸ Governance & success metrics leadership signed off
These are representative examples illustrating our scope. Swap in your real case studies, metrics, and client logos whenever you're ready.
“They were the rare partner who told us what not to build — then shipped the thing that mattered.”
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Bring us something hard. We'll tell you honestly how we'd approach it.