Learn / Evaluation guide
Private AI for your business: start with knowledge, coding, or one useful application
Srasta ·
A team looking at private AI can quickly end up discussing models, hardware, and infrastructure before agreeing on the work it wants to improve.
Start with the work. Perhaps employees repeatedly search internal documents. Perhaps developers want help explaining code or preparing tests. Perhaps an application needs to extract structured information from text.
These are different workflows. Each needs a suitable model, useful context, a way for people or applications to interact with it, and a clear way to assess the result. Private deployment can give a company more control over how these pieces operate, but someone still needs to own them.
Choose a problem you can evaluate
A useful first evaluation has a narrow boundary: one team, one task, and examples with outcomes you can assess.
For company knowledge, choose a small representative document set. Prepare questions whose answers you know, questions that require more than one source, and questions the documents cannot answer. Review the supporting sources and check that users cannot retrieve material outside their intended access.
For coding, choose a small repository or non-confidential sample. Try explaining a function, suggesting a bounded change, or generating tests. Have a developer review the output and run the relevant checks. A convincing-looking answer is not enough; the result must be useful in the development workflow.
For another application, define the expected output. If the task is extraction, specify the fields and examples that should remain empty when information is missing. If it is drafting, identify who reviews the draft before it is used.
Avoid starting with a promise to automate an entire department. A small evaluation makes failures easier to understand and improvements easier to measure.
Separate the model from the application
A coding model is not, by itself, an IDE or an autonomous coding assistant. A model endpoint is not automatically an employee chat application. Connecting tools introduces additional questions about credentials, permissions, execution, and approval.
Srasta's role is to provide a private AI platform: supported models, access controls, knowledge retrieval, and operating capabilities on infrastructure you control. The experience also depends on the compatible client or application you connect.
Before evaluating a specific workflow, confirm the supported model, hardware, runtime, and client. Check which capabilities the combination actually provides instead of assuming that an API-compatible connection supports every feature.
Understand the whole data path
Private deployment is a useful starting point for a data-boundary discussion, not the end of it.
Ask where inference happens, where documents are stored and retrieved, what the client sends elsewhere, and what any connected tools can access. Hosted licensing and catalog services have different roles from customer-local inference. Optional integrations and operational telemetry need their own review.
The right question is not simply whether a model runs locally. It is whether the complete workflow meets your requirements, with the connections and responsibilities made explicit.
Decide who will operate it
An evaluation needs an owner for installation, model selection, access configuration, updates, and troubleshooting. Your team's willingness to operate the system matters as much as the model's output.
Write down what success means before starting. For knowledge work, that may include finding the right source and handling missing information appropriately. For coding, it may include producing a useful change that passes review and relevant tests. For either, record the configuration and limitations so a result can be reproduced.
Include infrastructure and operating time when assessing cost. Software pricing alone does not describe the cost of running a private deployment.
Where memory and agents fit
Longer-running workflows may need persistent context: decisions already made, constraints that still apply, and work that remains unfinished. Srasta Membrane is a planned product direction for persistent, permission-aware context. That means managing stored context, not automatically retraining a model's weights.
We also plan an agent harness to help companies build and operate their own agents using models, tools, and memory. Permissions, human approval, execution boundaries, and recovery are requirements we want to understand with prospective users.
Both are planned capabilities, not products available today. An evaluation of Srasta should stand on the current workflow it can deliver.
Start with one outcome
You do not need to settle your entire AI strategy before evaluating a useful task. Choose knowledge, coding, or another application workflow; confirm the required components; and agree on what evidence would justify the next step.
Book a Srasta workflow discussion to discuss the outcome, deployment requirements, and a focused evaluation. Bring your questions; confidential documents or code are not needed to book.