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Compact private AI models fluent in your domain language.
Large public models are built to do everything. At high volume they can be slow and costly, and they can misread specialist terms or mixed-language text. We fine-tune smaller models inside your cloud, on your own examples, and your organization owns the result.
A smaller private model goes live only if it beats the standard one on your own test.
Large public models are general-purpose, and slow and costly at high volume. We test a standard model first, fine-tune a smaller one on your examples, and put it live only if it wins on a test set it has never seen. Unsure cases still go to a person.
Own a focused private AI asset built for high-volume operational work.
For repeatable, high-volume work such as classification, extraction, drafting or compliance screening, a smaller private model can match a large public model on accuracy while costing less and responding faster. We test that before training anything.
Benchmark standard models honestly first
We never recommend custom training for prestige. Week one establishes a rigorous accuracy, latency, and cost baseline using standard models so custom specialization must prove a clear operational advantage.
De-identify and structure examples in your cloud
All data preparation runs inside your environment. We remove personal identifiers, align labeling rules with your domain specialists, and lock away an untouched test set for fair grading.
Train compact, commercially clean private weights
Starting from proven open-license foundation models, we specialize the model on your terminology, formatting rules, and multilingual nuances until it decisively beats the baseline.
Pair fast automation with smart human escalation
We deploy calibrated confidence thresholds so clear routine cases process immediately at minimal cost while ambiguous or high-stakes items route cleanly to qualified staff.
A model you own, running in your own cloud.
Four to six weeks, all inside your cloud account.
- De-identified, specialist-curated training and locked benchmark datasets in your cloudGolden dataset
- Fine-tuned model weights, owned by youClient IP
- Head-to-head evaluation report comparing accuracy, latency, and operating costBenchmark
- Calibrated confidence router escalating uncertain cases cleanly to your specialistsSafe triage
- Repeatable retraining pipeline so your team can refresh the model as rules evolveContinuity
We fine-tune small models in-house.
Hands-on training experience
We curate datasets and fine-tune small privacy models ourselves. A model ships only after one read of a sealed test set, frozen before training began.
Complete data sovereignty
Sensitive enterprise, clinical, or public-sector records remain entirely inside your private infrastructure.
Specialist and mixed-language text
Built for industry terminology, customer conversations that mix languages, and strict output formats.
A go / no-go answer in week one
If structured prompting and guardrails solve your workflow without custom training, we tell you in week one and save your budget.
Five services, combined to fit the problem.
Readiness and governance
Prioritize high-return workflows & clear risk review
See this service02Legacy integration and agents
Connect current software without disruptive replacement
See this service03AI harness engineering
Confidential data protection, accuracy gates & budget caps
See this serviceCustom model fine-tuning
Compact models trained privately on your terminology
You are hereAI-driven development (ADLC)
End-to-end engineering loop with human release authority
See this serviceHow ready is your organization for AI?
24 questions across six pillars. You get a score for each pillar, your strongest and weakest areas, and the services that would close the gaps.
- 01Strategy & use-case readiness
- 02Enterprise architecture & integration
- 03ADLC & engineering practices
- 04Data & LLM training / fine-tuning
- 05AI harness & observability
- 06Governance, privacy & human sign-off
Do we need millions of historical records to train an effective private model?
No. We start from proven open-licence models, so a few hundred to a few thousand clean examples checked by your specialists is often enough. The week-one baseline tells you whether it is worth training at all.
Could external vendors or competitors ever access our trained model?
No. The dataset and the model weights stay in your cloud account, and our contract assigns them to you.
How does the model stay current as business rules change over time?
We hand over the retraining recipe, so your team can add verified staff corrections at each refresh. We can also run the refreshes for you under a support agreement.
Evaluate whether a compact, client-owned model fits your workload.
Book a 30-minute call
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