AI Agents That Handle Real Work in Your Real Systems
We build AI agents that take on defined, repetitive work, such as triaging requests, drafting responses, updating records and following up. They connect to the tools your team already uses, with clear limits and human review where it matters.
Core stackAzure OpenAI · OpenAI API · Azure AI Search · Semantic Kernel · Python · .NET / C# · Azure Functions · REST APIs
When an AI Agent Is Worth Building
Agents work best on repetitive, well-defined work where the rules and the data are clear.
An agent is only as good as its access and limits.
We define what it can see, what it can do and when a person must review its work, before we build it.
How we decideRequests pile up waiting to be sorted
Emails, tickets or enquiries sit in a queue until someone reads, categorizes and routes each one.
Your team repeats the same steps all day
Looking up information, filling in records and writing similar replies takes hours that could go to higher-value work.
Leads and customers wait for a response
Enquiries outside office hours or during busy periods wait too long for a first reply.
Answers are buried in documents
Staff spend time searching policies, product information or past cases to answer routine questions.
AI Agents We Design and Build
Pick one to see what it usually involves and how we typically build it.
Request triage and routing
Agents that read incoming emails, tickets or forms, classify them, extract the key details and route them to the right person or system.
- Email and ticket classification
- Data extraction into your CRM or helpdesk
- Priority and routing rules
Customer and lead response agents
Conversational agents that answer routine questions, qualify enquiries and hand over to your team with full context when needed.
- Website and chat assistants
- Lead qualification and handover
- After-hours first response
Knowledge assistants
Assistants grounded in your own documents and data, so staff get answers with the source shown instead of searching manually.
- Policy and product Q&A
- Answers with source references
- Access limited by role
Workflow agents
Agents that complete defined sequences, such as drafting documents, updating records or preparing follow-ups, across the systems they’re connected to.
- Drafting quotes, summaries or replies
- Updating CRM and business records
- Scheduled follow-up tasks
Integration into existing systems
Connecting agents to your CRM, helpdesk, email and business applications, with permissions that match each user’s role.
- CRM and helpdesk integration
- Role-based permissions
- Logged record of agent actions
Monitoring and improvement
Tracking agent accuracy, cost and outcomes after launch and improving prompts, data and rules based on real use.
- Accuracy and quality reviews
- Cost and usage monitoring
- Ongoing tuning
Define the Job. Then Build the Agent.
Every engagement follows the same problem-first Systems Engineering Method we use across AppPotter.
Pick the right task
We choose a well-defined, repetitive task where an agent can clearly save time.
Define limits and review points
What the agent can access, what it can do on its own and when a person approves.
Prepare data and connections
We connect the systems and knowledge sources the agent needs, with the right permissions.
Build and test on real cases
The agent is tested against real examples, including edge cases and failure scenarios.
Launch with human oversight
Rollout starts with review in place, so quality is proven before autonomy is increased.
Monitor and improve
Accuracy, cost and usage are tracked and the agent is improved based on real results.
What Changes When the Process Runs on the Right System
We agree on what to measure during discovery, so success is defined before the build starts, not after launch.
Before: Requests wait in a queue to be sorted
After: Requests classified and routed as they arrive
Before: The same replies are written by hand every day
After: Draft responses prepared for review, or sent within agreed limits
Before: Staff search documents for routine answers
After: Answers from your own knowledge, with the source shown
Before: Records are updated when someone finds time
After: Records updated as part of the workflow
Before: No visibility into what automation is doing
After: Logged actions, human review points and cost monitoring
Chosen for Your Systems, Not Our Preferences
We choose models and frameworks based on the task, your data and your security needs. Our implementation depth is in .NET on Azure or AWS, connected to leading LLM providers through their APIs.
Where we have the deepest production experience
- Azure OpenAILLM platform
- OpenAI APILLM platform
- Azure AI SearchRetrieval (RAG)
- Semantic KernelAgent framework
- PythonAI services
- .NET / C#Application layer
- Azure FunctionsAutomation
- REST APIsIntegration
Also used when the project calls for it
- Agent tooling
- Other models
- Data
Senior Engineers, Directly Involved
- Founder-led delivery
Parth and Umesh are personally involved in every engagement.
From scoping the work to standing behind its outcome, backed by Parth's 14+ years of enterprise technology and delivery experience.
Meet our leadership - Current engagement · under NDA
A long-term software engagement with a US-based technology company.
Client identity and project details are undisclosed due to confidentiality obligations. We treat your work the same way.
See our approach
Ways to Work Together
- Advisory & Discovery
Short-term Strategy Engagements
For a focused, short-term engagement - an assessment, an architecture review, or technical due diligence - before any build begins.
- Comprehensive architecture reviews
- Due Diligence of your tech stack
- Roadmap and Estimation
- Fixed Price
Fixed Cost Model
For clearly-scoped integration or build work, where the outcome and boundaries are already well defined.
- Change Management process
- Milestone-based delivery
- Quality & SLA Commitments
- Dedicated Team
Dedicated Product Engineering Team
For long-term product or systems work - a sustained AppPotter engineering team working against your roadmap, not rotated project to project.
- Seamless onboarding and roadmap planning
- Fixed Monthly Retainer
- PO/Tech-lead Collaborations
Whichever structure fits, Parth and Umesh are personally involved, from scoping the engagement to standing behind its outcome.
Meet our leadershipPart of AI & Automation
Applying AI and automation to real workflows, so routine work doesn't depend on someone remembering to do it. Agents are one of the most practical ways to apply AI: taking defined, repetitive work off your team’s plate.
Repetitive, well-defined work with clear inputs: triaging requests, answering routine questions, extracting data, drafting responses and updating records. Work that needs judgement, negotiation or accountability should stay with people, with the agent supporting them.
We limit what it can access and do, test it against real cases and put human review in place for anything with consequences. Autonomy is increased only once quality is proven.
Yes. Agents are most useful when connected to the CRM, helpdesk, email and business tools you already use, with permissions matched to each role.
We design around where data is processed and who can access it, using enterprise AI services and access controls suited to your requirements. How sensitive data is handled is agreed before the build.
Besides the build, there are ongoing model and hosting costs that depend on volume. We estimate these up front and monitor them after launch.
Sometimes rules-based automation or a simple integration is more reliable and cheaper. We’ll recommend that when it fits better.
Tell Us Which Task Your Team Repeats Most
We’ll tell you whether an AI agent, simpler automation or a process change is the right way to take it off their plate.
Book a GTM Systems Assessment30-minute call · No obligation · No sales pitch