AI Development
Model development, fine-tuning, and evaluation pipelines designed around your actual latency, cost, and privacy requirements, measured on your data before anything ships.
The approach, every time.
Constraint-first scoping
Latency, cost per inference, and data-privacy requirements set the design before any model is chosen.
Evaluation before deployment
Every model is measured against your real data and real failure cases, not a public benchmark that doesn't resemble your problem.
Built for monitoring
Drift detection and logging shipped alongside the model, so degradation is caught before it becomes an incident.
Deployed to your constraints
On-prem, private cloud, or your existing infrastructure, whichever your data and compliance requirements actually call for.
Illustrative examples, not a client list.
Solipher Labs is a young lab. These are the kinds of problems this service is built to solve, not named client work. Tell us what you're building.
Support that doesn't wait for business hours
An online retailer's support queue backed up every evening once the team went home, and most of the questions were the same handful of things. We built an AI support assistant trained on their catalog and policies, handling routine questions and escalating anything it wasn't confident about.
- Catalog & policy-grounded responses
- Confidence-based escalation to humans
- Continuous evaluation against real tickets
Turning scanned records into searchable data
A healthcare provider had years of patient records as scanned images with no way to search them. We built a document-processing pipeline that extracts structured data from scanned records, validated against a sample reviewed by their own staff before full rollout.
- OCR & structured field extraction
- Human-in-the-loop validation
- Privacy-compliant on-prem deployment
Forecasts that update with the week, not the quarter
A logistics company was forecasting demand quarterly, which meant the forecast was already stale by the time it shipped. We built a forecasting pipeline that retrains on rolling weekly data, cutting the lag between what actually happens and what the model expects.
- Rolling retraining pipeline
- Demand forecasting by route/region
- Drift monitoring & alerting
Catching the transaction that doesn't fit the pattern
A financial services firm's fraud rules were static and missed new patterns as fraud tactics shifted. We built an anomaly-detection pipeline that flags transactions statistically inconsistent with an account's own history, evaluated against their historical fraud cases before deployment.
- Account-level anomaly detection
- Evaluated on historical fraud cases
- Low-latency scoring at transaction time
Have something like this to build?
Tell us what you’re building and we’ll tell you honestly whether we’re a fit.