AI copilots & agents for operations

Teams buried in repetitive triage: support tickets, inbound leads, back-office checks, internal requests.

A chatbot that only talks doesn't remove work. The useful version reads your systems, takes structured actions and knows exactly when not to act — which is an engineering problem, not a prompt.

• +Workflow mapping: which steps are rules, which need a model, which need a person
• +Integrations with your tools and APIs (including MCP servers where useful)
• +Deterministic decision logic with a model for reading and drafting
• +Output guardrails, schema validation and a fallback path for every model call
• +A test suite built from your real cases
• +Deployed service, documentation and a short handover session

• Python
• FastAPI
• LangGraph
• LangChain
• MCP
• OpenRouter

I'll work with your existing stack where it makes sense — these are defaults, not requirements.

QueueStorm Investigator · Hackathon build · 2026

A support copilot for digital finance that can't make promises it shouldn't.

1. 01 · In / out

   Ticket + transactionsBangla, English or Banglish
2. 02 · Guard

   Request validationPydantic v2 schemas
3. 03 · Code

   Reasoning engineMatch, dedupe, route, set severity
4. 04 · Model

   LLM draftingAgent summary + customer reply only
5. 05 · Guard

   Safety post-processorNo PIN/OTP asks, no refund promises
6. 06 · In / out

   Structured decisionVerdict, department, human-review flag

1. 01

   Intro call

   30 minutes, free. You walk me through the workflow; I ask about your data, your systems and what “done” means.

2. 02

   Written scope

   A short document: what gets built, what's explicitly out, milestones, and a price. No surprises later.

3. 03

   Build in the open

   A demo on a live staging URL every week. You see working software, not status reports.

4. 04

   Handover

   Your repository, deployment, documentation and tests. Support after launch if you want it.

• →Deterministic where it must be right. Money, permissions, routing and anything with consequences is decided by plain code that can be tested and audited. Models read, interpret and draft — they don't get the final say.
• →Guardrails are code, not prompts. A system prompt is a request. Validation, schema checks and post-processing are guarantees. Every model output is checked before a user or a database sees it.
• →It keeps working when the model doesn't. Providers time out, keys expire, outputs drift. I design a safe fallback path for each model call so the product degrades gracefully instead of failing loudly.