ImadDhin Agentic System
AI agents built to run in production
Agents that act inside your real systems — with permissions, approval gates, evaluation, and cost limits you can audit. Scoped in a week, live in weeks, owned by your team.
- One scoping call before any build — you leave with a spec and a price.
- Agents ship against your data, tools, and access model, not a sandbox.
- You own the repository, prompts, evaluations, and deployment.
What we build
Custom agents on your stack
An agent that reads your systems of record, calls your tools, and hands off to a human when confidence drops.
- Tool use and function calling against your existing APIs
- Retrieval over your own documents, tickets, and product data
- Human approval gates on anything that writes, sends, or spends
Operations and support agents
Queue-level work: triage, routing, drafting, reconciliation, and follow-up that used to sit in someone's inbox.
- Intake classification with confidence thresholds
- Draft-then-approve flows before anything reaches a customer
- Escalation paths and audit trails on every action
Commerce and revenue agents
Discovery, product Q&A, and checkout assistance for storefronts, plus campaign and CRM automation that stays on brand.
- Catalogue-grounded answers instead of generic model guesses
- Campaign, CRM, and analytics wiring with brand guardrails
- Conversion and containment measured against a pre-agent baseline
Evaluation, safety, and cost control
The part most agent projects skip: proving the agent still works next month, and knowing what it costs per task.
- Regression suites over real historical cases
- Prompt-injection and tool-abuse testing before launch
- Per-task cost ceilings, rate limits, and spend alerts
How delivery works
- Step 1
Scope
One call to pick the task, the systems it touches, and the number we are trying to move. You get a written spec and a fixed price.
- Step 2
Prove
A narrow agent on one real workflow with real data, measured against how the work is done today.
- Step 3
Harden
Permissions, approval gates, evaluations, cost ceilings, and logging — everything needed for it to run unattended where that is safe.
- Step 4
Hand over
Your repository, your keys, your runbook. We stay on retainer only if you want us to.
Engagements
Every engagement opens with a scoping call, so you see the spec and the price before the build starts.
Agent discovery sprint
from $2k
Workflow audit, feasibility call, agent spec, and a priced build plan you can take to a board.
Scope an agent →Production agent build
from $5k
One agent live on a real workflow: integrations, approval gates, evaluations, monitoring, and handover.
Start a build →Agent platform / rescue
from $10k
Multiple agents, shared tooling, or taking over a prototype that cannot yet be trusted in production.
Talk through scope →Questions buyers ask first
Can you actually hire an AI agent to do work?
Yes for bounded, repeatable work with a clear definition of done — triage, drafting, lookups, reconciliation, follow-up. No for judgement calls with no ground truth. The first scoping call sorts your tasks into those two buckets before you spend anything.
How much does an AI agent cost to build and run?
Build starts at $2k for a scoped discovery sprint and $5k for a production agent on one workflow. Running cost is model usage plus hosting, which we cap per task during the build so you get a predictable monthly figure rather than an open-ended bill.
Is it cheaper to hire an agent than a person?
Per task, usually. Per outcome, only when the workflow is high volume and the failure cost is low or recoverable. We baseline the current cost and cycle time first, so the comparison is your numbers rather than a vendor claim.
Should we build a custom agent or buy a SaaS platform?
Buy when your workflow is standard and you can live with the vendor's data model. Build when the agent needs your proprietary data, your access rules, or a step no platform exposes. Most teams end up with a platform for the generic layer and one custom agent where the real advantage sits.
Do agents work with the tools we already have?
That is the normal case. Agents connect through the APIs and permissions you already run, and where an API is missing we design an explicit human step instead of pretending the integration exists.
How long does it take to go live?
A week to scope, four to eight weeks for a first production agent on one workflow. Prototypes are faster and routinely stall at the point where permissions, evaluation, and cost control start to matter.
Do agents need constant supervision?
They need monitoring, not babysitting. Anything that writes, sends, or spends gets an approval gate until its accuracy is proven; everything runs against a regression suite so you find drift before your customers do.
How do you measure whether the agent is working?
Against a pre-agent baseline on the same workflow: completion rate, human intervention rate, cycle time, cost per task, and escalation quality. If those numbers do not move, the agent does not ship.
Can a small business afford this?
Yes, if you start with one workflow instead of a platform. The discovery sprint exists so smaller teams can find out whether the return is real before committing to a build.
Can we try before committing?
Open the Agent workspace and use it free, or take the complimentary AI readiness scan for a written view of where automation pays off first in your stack.
Field notes on agents
The reasoning behind how we scope, migrate, price, and secure agent work.
AI agent impact on the market and our lives
Where agents already changed the economics of work, and where the claims still outrun reality.
The limits of agents built only from vibe-code tools
Why prototype-grade agents stall at scale, and what has to be rebuilt before production.
Migrating an existing business system to agents
A staged path from traditional or legacy workflows to agent-run operations without a rewrite.
Agent market value for builders
How to pick an agent product that is worth building, and how the value actually gets captured.
Securing agents in the most dangerous period of digital evolution
Prompt injection, tool abuse, and identity — the failure modes that decide whether agents stay deployed.
Bring one workflow and the number you want to move. You leave the first call with a spec, a price, and an honest answer on whether an agent is the right tool.