01
Good at the job.
The job you need.
Define what a good result looks like, then configure and evaluate the agent for that task. Our ambition is excellent results where they matter to your business.
Intelligence, precisely sized.
AI agents.
Built for
your business.
We build specialized AI agents that connect your business knowledge and tools, support everyday work, and fit environments you control.
Expert AI agents for smaller businesses.
Current work: policy checks and business workflowsPurpose over scale.Expert AI agents
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 the agent’s tools 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 workflow data within infrastructure you control, with the right configuration.
02 / A little more expert
Not every task
needs everything.
An AI agent combines AI, relevant business knowledge, and connected tools to carry out a defined task.
Start with a clear task. Connect the information and tools it needs. Set permissions and review steps, then evaluate the complete workflow against your business requirements.
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 an agent a specific job.
Connect the relevant knowledge and tools, define permissions and policy checks, and evaluate representative cases against the quality requirements you agreed.
Fit the agent 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.
A focused workflow needs careful implementation. Quality and total cost still need to be evaluated for your task.
03 / Featured work
Focused agents.
Clear boundaries.
Policy-aware workflows for AI agents.
When AI becomes part of your business, you need a clear view of how it follows your policies.
Our current work explores policy checks within AI workflows, helping teams identify outputs that need review before an action proceeds.
- The problem
- An AI application needs to follow the rules your business sets.
- Our focus
- A specialized agent with defined permissions, policy checks, and review steps.
- How we assess fit
- Your policies, representative examples, quality requirements, and deployment constraints.
COGNIMITE / AGENT GUARDRAILSCurrent work
01policy review layer
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. Capabilities are established through workflow evaluation.
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
The building blocks
of useful agents.
Agents rely on useful underlying technologies. These public examples span policy checks, document understanding, and computer use—and show why task-specific evaluation matters.
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 technologies and results belong to their publishers. We study them as potential building blocks for agents. Findings are publisher-reported; independent replication is not established by these sources.
06 / Close to your business
Local means
you choose where.
Deploy a suitable agent within infrastructure your business controls—on premises or in a private environment chosen for your needs.
Choose where the agent processes business information and which tools it can access. The right setup depends on your task, hardware, integrations, and operating requirements.
YOUR BUSINESS ENVIRONMENT
Business input
The information for your task
Your expert agent
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. The underlying AI, memory, processing capacity, response-time needs, and concurrent usage all matter. An agent’s hardware requirements depend on its components and workflow.
Does local deployment keep all data private?
Local processing can reduce the need to send business information 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 agent 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 specialized AI agents for smaller businesses. Our current work focuses on business workflows, policy checks, 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)