Keep sensitive data inside your own walls. ASCENDING helps teams scope domain-specific models, fine-tune with company data, and deploy privately in your own cloud or on-premises environment — never a third-party foundation model.
Financial services, healthcare, and legal face the same three barriers when they try to put a foundational LLM to work in corporate standard.
Regulated businesses can't send sensitive data to third-party foundation models like GPT or Anthropic's. Leaking PII or PHI to an external provider is a non-starter — the model has to run in your own private environment, cloud or on-premises.
A foundational model is built for everything and tuned for nothing in particular. A model fine-tuned on your own data performs your specific task more accurately than a general-purpose one.
A smaller, task-tuned model responds faster and uses fewer tokens per request than a general-purpose foundation model — lowering both latency and inference cost at scale.
One flow, from your siloed data to a governed private deployment.
Built on smaller, open-source LLMs. Fine-tune the base model on your own examples, or distill it into an even smaller model scoped to one task — a model trained on historical trading records, for instance — then deploy it into your own private environment, cloud or on-premises, without the cost or scope of a full training pipeline.
A generic fine-tune isn't domain-aware. Ours are scoped to the workload from day one, using the language and structure of your industry — financial services, healthcare, and legal.
For larger customers who want to train and fine-tune their own models in-house. Includes the data readiness work enterprises need first — turning siloed, unstructured company data into a usable training set for an open-source LLM.
A fine-tuned or distilled model still needs to reach your enterprise tools and current documents. We connect it to retrieval-augmented generation and a governed knowledge base, with the same access controls and audit trail as the rest of your AI stack.
Jarvis Registry governs how any model reaches your MCP servers, agents, and knowledge base through one endpoint.
Discuss your task, data readiness, deployment requirements, and success criteria with ASCENDING. Start with a defined problem and a scope your team can evaluate.
Reference facts covering model base, fine-tuning, deployment, data handling, evaluation, and handoff.
| Capability | What it provides |
|---|---|
| Model base | Smaller open-source LLMs, selected against task quality, license terms, and hardware needs. |
| Fine-tuning | Task-specific adaptation using your own examples — not training a foundation model from scratch. |
| Deployment | Your own cloud environment or on-premises infrastructure. Model weights and inference stay inside your boundary. |
| Data handling | Training data, embeddings, and evaluation sets stay under your data-governance rules — no PII or PHI reaches a third-party model. |
| Evaluation | The adapted model is compared against a baseline using representative examples, edge cases, and agreed review criteria. |
| Handoff | Code, model artifacts, documentation, and access delivered to the team that will operate the result. |
These are procurement questions to have answered before commissioning the work — not assumptions about what any provider includes.
| Specification | Value |
|---|---|
| Model & license | Named open-source base model and version; license terms cover use, modification, and redistribution of fine-tuned artifacts. |
| Inference location | Customer cloud account or on-premises infrastructure — never a third-party model endpoint. |
| Training & retrieval data | Datasets, embeddings, indexes, and backups stored inside the customer's environment, with defined access and deletion. |
| Logs & audit | Inference and training logs retained inside the customer boundary; diagnostic access is time-limited and audited. |
| Release process | Named approver for each model version, with a tested rollback path for a failed release. |
| Handoff deliverables | Code, model artifacts, configuration, runbooks, and documentation transferred at project close. |
The engagement starts with the workload and data. These 6 steps take it from a defined problem to a handed-off system.
Workload and baseline. Define the input, expected output, current approach, and business acceptance criteria.
Data readiness. Review usable examples, permission to use the data, sensitive fields, labeling consistency, and a separate evaluation set.
Model selection and fine-tuning. Evaluate smaller open-source model candidates against task quality, license terms, hardware needs, and operating cost.
Evaluation. Compare the adapted model with the baseline using representative examples, edge cases, and agreed review criteria.
Deployment and integration. Specify where model weights and inference run, which systems connect, and whether any external services receive data.
Handoff. Agree on code, model artifacts, documentation, access, and the team responsible for future changes.
ASCENDING helped D2 Nova turn call transcripts into summaries, follow-up tasks, and searchable customer context. The implementation used more than 8,000 transcript-summary pairs for model fine-tuning, SageMaker inference, and a hybrid RAG pipeline.
Read the D2 Nova case study
Questions to settle before commissioning the work.
Not necessarily. Fine-tuning an existing smaller open-source model is one option for adapting it to a defined task. Start by comparing that option with prompting and retrieval. Training a new foundation model is a different undertaking and should not be assumed to be part of a fine-tuning project.
There is no single minimum that fits every task. The useful starting point is a representative sample with consistent inputs and reviewed outputs, permission to use it, and examples reserved for evaluation. Data quality, task complexity, and the baseline model affect what is needed.
Yes. Retrieval and fine-tuning can be combined. Retrieval supplies relevant source material, while fine-tuning adapts the model to the task. The evaluation should show whether the combination improves the business result enough to justify its added complexity.
Agree on those items explicitly in the project scope and contract. Separate your data and custom code from the base model's license, adapted model artifacts, deployment configuration, and documentation. Ownership and permitted use should not be inferred from the term open source.
No. Test the model on held-out examples and realistic failure cases. Define what counts as an acceptable answer, when a person should review it, and how quality will be checked after release. Fine-tuning alone is not an accuracy guarantee.
Bring one priority workflow, sample inputs and expected outputs that you are permitted to share, current failure examples, hosting constraints, and the team that will operate the result. These details make the scope more useful than a general request to build an LLM.
ASCENDING builds on published protocols, licenses, and regulatory frameworks rather than proprietary lock-in. Reading the underlying standards is the fastest way to judge how a deployment holds up under audit.
