Site.nu
AI Agent Development

AI agent development and workflow automation for teams with repetitive knowledge work

Many teams want to do something with AI, but they lack a safe path from use case to production. We design AI workflows and agents for processes with clear input, decision rules, and human oversight. Think of intake and triage, document processing, knowledge assistants, review loops, and operational follow-up.

In a 30 to 45 minute AI strategy session, we look at which processes are promising, where the risks sit, and how to start small without staying superficial. You get a fit check and a concrete next step, not a loose inspiration session.

Start with the right use case

Human in the loop where needed

From pilot to production

Working with teams that take automation seriously

Which processes are and are not a good fit for AI agents

Not every process needs AI. This service is mainly suited to repetitive knowledge work with a lot of manual interpretation, classification, summarization, or follow-up. For simple, fully deterministic flows, traditional automation is often the smarter option.

High volume, high repetition

Similar tasks, questions, documents, or requests keep coming in and are currently read, processed, or forwarded manually.

Clear input and boundaries

There is enough context, data, or documentation to ground an agent, and you can define and test the desired output clearly.

Human review remains possible

You want to use AI to gain speed, but not at the expense of control. That is why we design review, logging, and escalation into the flow.

What we can design and build for your team

We work from concrete processes and use AI only where it adds real value. Below are the use cases that are most often promising for a first pilot or production flow.

Intake and triage

Automatically summarize, classify, and route new requests, tickets, or internal asks into the right flow.

Document processing

Extract, structure, validate, and pass information from documents into the next step or system.

Knowledge assistants

Answer internal or external questions based on controlled sources, policies, manuals, or customer context.

QA and review loops

Check text, output, or cases for completeness, tone, deviations, or missing information.

Follow-up workflows

Trigger actions after an event, such as sending summaries, creating tasks, or preparing follow-up questions.

Integrations and monitoring

Connect AI output to existing tools while keeping visibility into errors, exceptions, and human intervention.

From process scan to controlled rollout

We do not start with model choice, but with the process, the risk, and the desired outcome.

01

Process scan and use-case selection

We analyze where repetitive knowledge work sits, how decisions are currently made, what data is available, and which risks need attention.

Pilot design and guardrails

We choose a promising flow and design prompts, decision logic, evaluation criteria, review moments, and system integrations.

Build, test, and validation

We build the workflow or agent, test with real examples, and improve for quality, reliability, and exception handling.

Rollout and operations

After validation, we set up monitoring, feedback, versioning, and ongoing development so the solution remains useful in production.

AI agent approach versus traditional automation

Use AI where interpretation is needed. Use rules-based automation where the logic is fully fixed.

AI + workflow orchestration

Best suited for variable input, interpretation, and support for human work

Traditional automation

Strong for fixed rules, fixed fields, and fully predictable processes

Handling unstructured input
Well suited to text, documents, context, and variable phrasing
Works best with fixed fields and fixed rules
Building in human review
Review and escalation can be an explicit part of the flow
Also possible, but without AI support on content and interpretation
Deterministic tasks
Not always necessary when the process is completely fixed
Often the best choice when rules are fully unambiguous
Using knowledge and context
Can use sources, instructions, and prior context
Limited without a lot of custom logic
Risk and governance
Manageable, but it requires explicit evaluation, logging, and guardrails
Less variation, but also less suited to interpretive work
Testing value quickly in a pilot
Strong when you want to validate a scoped use case first
Strong when the process is already tightly and predictably defined

Frequently asked questions

Answers about use-case selection, risk, privacy, and how to move from pilot to production.

Schedule an AI strategy session about your process

In 30 to 45 minutes, we determine which use case is the most promising, where the risks are, and how to start small without staying superficial. You leave with a fit check and a concrete next step.

Briefly describe the AI use case you want to explore

Schedule your session

  • A short live walkthrough of the platform and approach
  • Straight advice on what does and does not fit your situation
  • A concrete next step you can act on right away

Schedule an AI strategy session

Let us know which process currently costs a lot of manual work, where many documents or requests come in, or where you think AI could help. That way we can prepare the call in a focused way.