AI Agents & Automation

AI agent development services: custom agents and apps that work inside your systems.

For teams whose work spans tools, documents and rules that no off-the-shelf product understands. We build agents and AI features that read your data, use your tools with limited permissions and show their sources, so your team can check every answer.

Book a free custom AI agent call See it in action

A person replies within one business day
A plan you can act on
You own every asset

Time to first results: Live pilot in 4–8 weeks
Engagement: Setup + monthly support
Best for: Teams with their own data and rules

Reviewed by Noor KhanUpdated November 2026

Custom agent· Tools · permissions

Sample

A task reaches your agent
It reads your policy
Acts within its permissions
You approve the reply

Illustration with sample data. Your version uses your accounts and numbers.

Brands Noor has worked with

Overview

What is custom AI agent development?

Custom AI agent development is building software that uses a language model to complete a specific job with your own data and tools, such as answering staff questions from internal documents, qualifying leads or updating your CRM. It differs from a general AI assistant because it's connected to your systems, follows your business rules and is tested against your real cases. Noorscale designs, builds and monitors these agents and AI features. Each one ships with permission limits, source citations and human approval for sensitive actions.

See it in action

What does a custom AI agent look like when it's working?

When a custom AI agent is working, you see four changes: answers in seconds, with sources; hours of manual work removed; AI that follows your rules; and a system you own outright. The live sample below is built with illustrative data. Your version runs on your own accounts and your numbers.

Custom AI agent · ask your docsInternal knowledge assistant · permission-aware

Sample

Asking as

Knowledge base

5 of 6 visible

Refund policyPolicies
Billing approvalsFinance SOPs
Client onboarding checklistOperations
Contracts and access rulesLegal
Security and SSO FAQProduct
Pricing playbookSales only · hidden for your role

Searched 5 documents you can access · 1 hidden by permissions

Knowledge assistant

Illustrative documents and answers. Your agent reads your own files with your access rules, cites every answer and hands off when nothing approved covers the question.

Sound familiar?

What problems does a custom AI agent solve?

It solves three common problems: “Our team spends hours hunting for answers we already have”, “Our AI pilot looked great in the demo and broke in real use” and “Off-the-shelf AI tools don't know our business”. Each one has a fix.

The problem

“Our team spends hours hunting for answers we already have”Policies, contracts, specs and past tickets live in five places. People ask a colleague instead, and the answer depends on who they ask.
“Our AI pilot looked great in the demo and broke in real use”Real users ask off-script questions, tools time out and nobody measured accuracy before launch, so trust disappeared within weeks.
“Off-the-shelf AI tools don't know our business”Generic assistants can't see your systems, don't follow your rules and can't take action, so staff copy and paste between tabs.

What changes

Answers in seconds, with sourcesStaff and customers get answers from your own documents, each linked to where it came from, instead of waiting on a colleague.
Hours of manual work removedReading, sorting, summarizing and updating records run automatically, with people approving the sensitive steps.
AI that follows your rulesThe agent sees only what it's allowed to see and does only what it's approved to do, so nothing surprises your team.
A system you own outrightCode, prompts and tests stay with you, and models can be swapped as better ones arrive without starting over.

Compare your options

Off-the-shelf AI tool, an in-house build or a custom agent?

Each option suits a different situation: off-the-shelf AI tool (a tool already covers the job), build it in-house (you have AI engineers with time), and custom agent by Noorscale (the job spans your systems and rules). Here's how they compare on the points buyers ask about most.

Off-the-shelf AI tool, an in-house build or a custom agent?
Compared on	Off-the-shelf AI tool	Build it in-house	Custom agent by Noorscale
Time to first results	Off-the-shelf AI toolFastest to switch on	Build it in-houseDepends on your team's AI experience	Custom agent by NoorscaleLive pilot in 4–8 weeks
Cost to start	Off-the-shelf AI toolLowest, usually a subscription	Build it in-houseEngineer time and hiring	Custom agent by NoorscaleSetup fee plus monthly support
Fits your process	Off-the-shelf AI toolYou adapt to the tool	Build it in-houseFully, if the skills exist	Custom agent by NoorscaleBuilt around your rules and systems
Testing before release	Off-the-shelf AI toolThe vendor's own checks	Build it in-houseOnly if your team builds it	Custom agent by NoorscaleEvaluation suite on your real cases
Who owns it	Off-the-shelf AI toolThe vendor	Build it in-houseYou do	Custom agent by NoorscaleYou do, with code, prompts and tests
Best when	Off-the-shelf AI toolA tool already covers the job	Build it in-houseYou have AI engineers with time	Custom agent by NoorscaleThe job spans your systems and rules

On the free call, Noor will tell you which option suits your stage, even when it's one of the others.

What's included

What does a custom AI agent include?

It includes six deliverables: use-case brief and success criteria; answers from your documents, with sources; tool and system integrations; evaluation test suite; an interface your team will use; and monitoring, cost tracking and runbook. Each one is working material your team can use, not a slide deck.

Book a free custom AI agent call

Scope · Custom AI Agents & Apps6 deliverables

01
Use-case brief and success criteria
What the agent does, what it must never do, which systems it touches and how we'll judge it, agreed in writing before we build.
02
Answers from your documents, with sources
Your documents and records indexed with access rules intact, so people only get answers from content they're allowed to see, each with a citation.
03
Tool and system integrations
Scoped connections to systems such as your CRM, help desk, database or ERP, with read and write limits per action and approval steps for sensitive changes.
04
Evaluation test suite
Real cases scored before every release for correct answers, correct actions, refusals and handoffs, so an update never quietly breaks behavior.
05
An interface your team will use
A chat panel, a Slack or Teams bot, or a feature inside your existing app, whichever fits how people already work.
06
Monitoring, cost tracking and runbook
A trace of every run, cost and latency alerts, and a runbook to pause, roll back or switch models without a rebuild.

Tools and platforms we work in

Claude
OpenAI GPT models
Google Gemini
Model Context Protocol (MCP)
LangGraph
LlamaIndex
PostgreSQL with pgvector
Langfuse
Vercel AI SDK
Next.js

Process

How long does a custom AI agent take?

The work runs in four steps, starting in weeks 1–2 and then running as ongoing work. Every step ends with something you can use, so you always know what's landed and what comes next.

Time to first results: Live pilot in 4–8 weeks

Step 01Weeks 1–2
Pick one job and define success
We map the workflow, choose one high-value task, write the success criteria and list every system the agent reads from or writes to.
You get: Written use-case brief and success criteria
Step 02Weeks 2–5
Build the first version
We build the agent, connect tools with scoped credentials and assemble the test set from your real questions and records.
You get: Working agent and a test set from your cases
Step 03Weeks 5–8
Test and pilot with real users
The agent passes the test suite, then goes live for a small group with people reviewing its actions until results hold.
You get: Supervised pilot live with a small group
Step 04Ongoing
Monitor, extend and hand over
We review traces and failed cases, grow the test set, report monthly on accuracy and cost, and document everything for your team.
You get: Monthly accuracy and cost report, full documentation

Reporting

How will you know a custom AI agent is working?

You'll know a custom AI agent is working from four measures on a live dashboard: task success rate, evaluation pass rate, hours saved per week and cost per task. You also get a written review every month: leading indicators first, then the numbers your finance team cares about.

Monthly reportSample

Task success rateHigher is better: Share of jobs the agent completes correctly without a person stepping in.
Evaluation pass rateHigher is better: Score on the test suite before each release, tracked over time to catch regressions early.
Hours saved per weekHigher is better: Measured against how long the task took before launch, from your team's own logs and estimates.
Cost per taskLower is better: Model and hosting cost for each completed job, so the economics stay visible as volume grows.

Free toolAutomation Time CalculatorAdd up the hours and cost of repetitive tasks you could automate, starting from tasks typical for your industry.Use it free

15 minutes with Noor

Noor Khan, Founder & CEO

Free

You'll leave with

Your biggest growth opportunity, in plain terms
The first two or three moves worth making
A clear plan for your first milestone

Noor listens first, then shows you the fastest route to the result you want.

Useful to have with you

Your website address
The goal or problem you want to solve
Any numbers you already track

See available times

Booking opens on Cal.com. Or send a call request

Who it's for

Who is a custom AI agent for?

It works best for teams with knowledge spread across documents, tickets and tools; companies with a repeatable task that follows clear rules; and two more listed here.

Teams with knowledge spread across documents, tickets and tools
Companies with a repeatable task that follows clear rules
Product teams adding AI features to an existing app
Operations with enough volume that minutes per task add up

Engagement

How is a custom AI agent scoped and priced?

Pricing is shared after the free call, in a written scope with deliverables, timeline and fee. Never a package, never a surprise.

Book a free custom AI agent call

Monthly retainer
Ongoing work against a 90-day plan, for channels that compound month over month.
- Monthly report and strategy call
- Priorities reset every quarter
- No long lock-in
Typical for this service
Fixed-scope project
A defined scope, timeline and fee for builds, audits, migrations and launches.
Usually: Setup + monthly
- Deliverables agreed in writing
- Milestones you can check
- Optional care plan after launch
Focused sprint
A short, time-boxed piece of work on one priority, to prove the approach first.
- One clear goal
- Working output, not a deck
- Roll into a retainer or stop

Every engagement includes

One production agent or AI feature
Integrations with scoped permissions
Evaluation suite and supervised pilot
Monitoring, alerts and monthly tuning

What shapes the scope

How many systems the agent reads from or writes to
Whether it only reads data or also takes actions
Review needed for regulated or sensitive data
Where your team uses it, such as Slack or your own app

You'll see how each one applies to you in the written scope.

Who leads the work?

Your custom AI agent, led by Noor Khan

A software engineer turned AI and growth strategist. Noor sets the strategy and architecture on every engagement, reviews every plan before it ships and built the method the whole team works by.

FAQ

Questions about Custom AI Agents & Apps

Something else on your mind? Email grow@noorscale.com and a person will reply within one business day.

How much does a custom AI agent cost?

It's a one-time build fee plus a monthly fee for monitoring, tuning and support. The build depends on how many systems the agent touches, whether it writes data or only reads it, and how much review regulated data needs. Model usage is billed at cost and tracked per task. You'll get a fixed written scope after a free call.

Should we build a custom agent or buy an off-the-shelf AI tool?

Buy if an existing tool already covers your workflow and data, because it's cheaper and faster. Build when the job spans several of your systems, depends on your own rules or has to hand off in a specific way. We'll tell you which one fits during scoping, even if the answer is a tool you already own.

Can an AI agent answer questions from our internal documents?

Yes, and it cites the document and section behind every answer so your team can check it. It respects existing permissions, so people only get answers from content they're allowed to see. When nothing approved covers the question, it says so instead of guessing.

How do you stop the agent from making things up?

We ground answers in your approved sources, require citations, limit which actions the agent can take, and score it against real cases before every release. Low-confidence answers go to a person. After launch, every failure we find is added to the test set.

Can the agent take actions, such as updating our CRM?

Yes, through scoped connections that allow only the specific actions the job needs. Sensitive actions, such as sending an email to a customer or changing a deal value, can require a person's approval first. Every action is logged, so you can see who or what changed a record.

Which AI models do you build on?

We build on models such as Claude, GPT and Gemini, and choose per task based on test results, cost, speed and data terms. The integration is designed so the model can be swapped later without a rebuild.

Is our data used to train AI models?

Your data stays out of model training. We use business API terms under which providers don't train on your inputs or outputs. Credentials sit in a secrets manager, never in prompts, and data stays in your own cloud accounts wherever possible.

Who owns the code and the agent?

You do. The code lives in your repository, runs in accounts you own, and the prompts, tests and documentation are handed over with it.

How long until we have something working?

Most single-task agents reach a supervised pilot in 4–8 weeks. We widen access once the test results and real-world behavior hold up, so you see value early without betting the whole team on it.

Related services

What pairs well with Custom AI Agents & Apps?

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