abeeba
Resources · Case Studies
How our engagements work, step by step.
These walkthroughs describe typical projects for each type of client. Client names are withheld and published results will be added with permission.
Sample engagement · Fintech lender
Private LLM for underwriting analysts
Analysts were pasting credit policy and application details into a public chatbot. Compliance wanted it stopped, but the team relied on it.
What we do
- Two-week assessment of use cases, data classes and expected load
- Benchmark of three open-weight models on 200 real analyst questions
- Deployment inside the lender's existing cloud account with SSO and audit logs
- Retrieval over credit policy documents with source citations
- Rollout to one team, then the whole department
What the client gets
- A private assistant that never sends data outside the lender's account
- Audit logs of every question and answer for compliance review
- An internal endpoint other teams can reuse
Sample engagement · Healthtech platform
Cutting inference cost per patient summary
The platform generated summaries with a large model on dedicated GPUs. Volume grew every month, and so did the bill, while GPUs sat idle at night.
What we do
- Telemetry on every GPU and model for two weeks
- Continuous batching and FP8 quantization, tested against clinician-rated samples
- Fine-tuned smaller model for routine summaries, large model for complex cases
- Autoscaling so capacity follows daily demand
- Chargeback dashboards per product line
What the client gets
- Measured cost per summary, tracked monthly
- Quality checked against a clinician-reviewed evaluation set
- Finance and engineering looking at the same numbers
Sample engagement · Logistics group
From spreadsheets to an MLOps platform
Demand forecasts were produced by notebooks on one data scientist's laptop. Nobody else could rerun them, and errors were found only after orders went out.
What we do
- Lifecycle mapping with planning and data teams
- Experiment tracking, model registry and orchestration on the group's cloud
- Automated weekly retraining with evaluation gates
- Drift and accuracy monitoring with alerts to planners
- Templates so new models follow the same path
What the client gets
- Reproducible forecasts anyone on the team can rerun
- Bad models blocked before they reach planners
- A platform for the next models, not just this one
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