01
Good at the job.
The job you need.
Define what a good result looks like, then train and evaluate for that task. Our ambition is frontier-level performance where it matters to your business.
Intelligence, precisely sized.
Expert AI.
Built for
your business.
We build compact, specialized AI models designed to run locally, keep data under your control, and fit practical budgets.
Expert Language Models for smaller businesses.
Current work: compact guardrail models · ≈4B parametersPurpose over scale.Expert Language Models
01 / Built around your business
The right fit
changes everything.
Useful AI starts with what your business needs to get done. We focus on three things that make it worth doing.
Good at the job.
02
Practical costs.
Room to grow.
Match model size and deployment to real demand. Evaluate the whole cost: hardware, integration, operation, and ongoing care.
03
Your data.
Your environment.
Choose where processing happens. Local deployment can keep model inputs and outputs within infrastructure you control, with the right configuration.
02 / A little more expert
Not every task
needs everything.
An Expert Language Model is our term for a small language model specialized for a particular job.
Start with a clear task. Use relevant examples. Evaluate against the quality your business needs. A narrower focus gives the work a meaningful target.
A clear task is the starting point.
For example: check whether an AI response follows your written policy. Agree on the policy, the inputs, and what counts as a useful result.
Give a smaller model a specific job.
Use relevant examples to specialize a model for the task. Evaluate on representative cases, including difficult ones, against the quality requirements you agreed.
Fit the model to the way you work.
Choose suitable hardware and integrations, then test the complete application. Decide where data is processed and how results, monitoring, and human review are handled.
Size is a design choice, not a guarantee. Quality and total cost still need to be evaluated for your task.
03 / Featured work
Small model.
Clear boundaries.
Compact guardrails for AI applications.
When AI becomes part of your business, you need a clear view of how it follows your policies.
We develop guardrail models of approximately 4 billion parameters, designed to help AI applications follow defined policies, with practical deployment in mind.
- The problem
- An AI application needs to follow the rules your business sets.
- Our focus
- A compact, specialized model with a defined policy-following role.
- How we assess fit
- Your policies, representative examples, quality requirements, and deployment constraints.
COGNIMITE / GUARDRAIL WORKCurrent work
≈4Bparameters
A focused role within your AI application.
A focused role within your AI application.
A guardrail is one layer in an application. It cannot eliminate every unsafe or incorrect result.
An example to make it concrete
Your policy becomes
the evaluation question.
Consider an AI assistant answering customer questions. A useful guardrail has a specific policy to check against.
Illustrative business policy
“Do not promise a refund before a team member has reviewed the request.”
Question to evaluate
Illustrative use case. This is not live inference or a reported Cognimite test result.Does the assistant’s reply make an unapproved refund commitment?
Built around your requirements.
We’ll discuss the policies, deployment constraints, and evaluation needed for your use case. No Cognimite benchmark results are published here.
04 / Exploring what comes next
A voice assistant
built around
your business.
Research directionWe’re exploring a local voice-and-action assistant for smaller businesses. A way to find information in your documents, prepare routine work, and carry out approved actions in the tools you use.
Our aim is to bring voice, document understanding, and computer use into an assistant your business can operate within its own environment.
Discuss your workflowOne request. A useful next step.
“Find the latest supplier invoice, check it against the purchase order, and prepare the details for my review.”
- 01Find the context
The relevant invoice and purchase order.
- 02Prepare the work
A draft record with differences highlighted.
- 03Keep you in control
Your review before the next action.
Ask your documents.
Explore answers grounded in selected internal files, with references to the information behind them.
Prepare routine records.
Turn repeat documents or spoken job notes into structured drafts your team can review.
Move work forward.
Explore approved steps across business applications, including interfaces that require clicks and typing.
We’re evaluating these possibilities. Supported workflows, hardware requirements, and performance will be established through development and testing.
05 / Examples from the wider field
Small models.
Worth a closer look.
Specialization is already doing interesting work. These public examples show why we pay attention—and why the task and evaluation matter.
01 POLICY CLASSIFICATION
A focused role in content safety.
NVIDIA’s Nemotron Content Safety Reasoning 4B classifies prompts and responses against content policies. Its reported results show the potential of a compact model on a defined moderation task.
Evaluation context
The model card reports average harmful-class F1 of 0.868 on custom-policy evaluations using CoSApien and Dynaguardrail, with reasoning enabled. F1 is a classification metric, not an accuracy percentage. The evaluation date is not separately stated. Results are publisher-reported and do not imply flawless moderation.
02 DOCUMENT RETRIEVAL
Find the right page. Even when it’s visual.
Nemotron ColEmbed V2 specializes in retrieving visually rich document pages. NVIDIA reports that its 8B variant led the ViDoRe V3 leaderboard in the paper’s February 3, 2026 snapshot.
Evaluation context
The 8B variant achieved average NDCG@10 of 63.42 on ViDoRe V3; the 4B variant ranked third at 61.54. These are benchmark-specific retrieval results, with compute and storage tradeoffs. Variant names are 3B, 4B, and 8B; table counts excluding embedding weights are 3.99B, 4.43B, and 8.14B. The paper was submitted February 3 and revised April 1, 2026. Exact test execution dates are not supplied.
03 STRUCTURED EXTRACTION
From business documents to usable data.
NuMind’s NuExtract3 is a 4B vision-language model for document-to-JSON extraction and document-to-Markdown conversion. It illustrates the possibilities of designing a model around a specific workflow.
Evaluation context
NuMind reports stronger structured-extraction results than the similarly sized models it tested on its own benchmark of about 600 challenging extractions across 15 problems. Its OCR evaluations use publisher-selected methods, including an LLM judge. The evaluation date is not separately stated. These results do not establish performance on every business document.
04 BROWSER ACTIONS
From a request to a browser action.
Microsoft’s Fara1.5 family specializes in using web browsers through screenshots. Its downloadable models show how focused agents can combine interface understanding with actions such as clicking, typing, and scrolling.
Evaluation context
Microsoft reports 80.8% task success on WebVoyager and 57.3% on Online-Mind2Web for Fara1.5-4B. These are browser benchmark results, not general desktop accuracy or Cognimite results. Public weights became available July 22, 2026. The documented serving path uses GPUs; performance on customer hardware requires evaluation.
05 COMPUTER USE
Specialists that work with interfaces.
GUI-Owl 1.5 explores actions across desktop, mobile, and browser interfaces. Its work highlights both the promise of specialized computer-use models and the importance of testing a complete task.
Evaluation context
The model card reports 52.3% task success on OSWorld-Verified and 69.0% on AndroidWorld for GUI-Owl-1.5-8B-Instruct. Test execution dates and step budgets are not stated alongside that table. These results use different tasks and protocols from browser-only evaluations and should not be directly compared. Local serving is documented; a laptop performance guarantee is not established.
These are publisher-reported findings from the wider field, not Cognimite models, partnerships, or results. Independent replication is not established by these sources.
06 / Close to your business
Local means
you choose where.
Run a suitable model within infrastructure your business controls—on premises or in a private environment chosen for your needs.
You decide where the model processes business information. The right setup depends on your task, hardware, integrations, and operating requirements.
YOUR BUSINESS ENVIRONMENT
Business input
The information for your task
Your expert model
Processing within your environment
A useful result
Ready for the next step in your work
Local deployment creates choices.
Good implementation makes them meaningful.
Will it run on our existing hardware?
That needs evaluation. Model size, memory, processing capacity, response-time needs, and concurrent usage all matter. A compact model does not automatically run well on every laptop.
Does local deployment keep all data private?
Local processing can reduce the need to send model inputs and outputs to external services. Data handling also depends on connected applications, logs, telemetry, backups, access controls, and network settings. Local deployment alone does not guarantee security.
What should we evaluate before deployment?
Use representative examples to assess quality, error cases, latency, hardware fit, and total operating cost. Agree on human review, monitoring, updates, and the policies your application needs to follow.
07 / Start with one valuable task
Big possibilities.
A focused first step.
Bring a task your team does again and again. We’ll talk through whether a specialized model fits, and what a useful evaluation would look like.
Speak with Shubham Kothari.
Contact on LinkedIn (opens in a new tab)A conversation about fit. Start wherever you are.A few good things to bring
01
A repeated business taskWhat takes time, attention, or a lot of repetition?
02
Your quality requirementsWhat does a good result look like? What can’t go wrong?
03
Your deployment constraintsWhere should data be processed, and what needs to connect?
About Cognimite AI
Cognition + mite.
Founded in 2025, Cognimite AI develops compact, specialized language models for smaller businesses. Our current work focuses on guardrail models and practical local deployment.
Our name brings together cognition and mite: intelligence in a small, focused form. Expertise built around a job that matters.
Shubham Kothari on LinkedIn (opens in a new tab)