Why is Discovery paid?

Discovery is paid because the work produces real consulting artifacts your team can use. We map workflows, interview stakeholders, review systems, identify bottlenecks, and build an implementation roadmap. That is different from a sales call where the only output is a proposal. Charging for Discovery protects your team's time and ours.

Can we use the Discovery output without hiring CloudNSite to build?

Yes. You own the Discovery deliverables whether or not we build. If you decide to implement internally, use another AI agent development company, or pause the project, the workflow maps, roadmap, scope, and ROI estimates are still yours. The point is to give you a clear operating plan, not lock you into a build.

Do we need a Pilot before Production?

No, but most engagements start with one well-scoped workflow before expanding. A Pilot gives your team a contained way to test the approach, validate adoption, and measure whether the custom AI automation creates enough value. If the opportunity is already clear and the workflow is mature, we can scope a Production engagement directly.

How is this different from Zapier, Make, or n8n?

Zapier, Make, and n8n are strong for connecting apps and automating predictable steps. CloudNSite builds custom software systems around your workflow. That means deeper integrations, custom logic, evaluation, deployment control, and edge case handling that is designed for your business.

How is this different from Lindy or Relevance AI?

Lindy and Relevance AI can be good fits for quickly configuring AI-assisted workflows. CloudNSite is different when the work needs custom architecture, owned code, private deployment, specialized integrations, or evaluation beyond platform defaults. We build the system around the process, not the other way around.

Yes, when the engagement is scoped as a client-owned build. You can receive source code, documentation, deployment materials, and the evaluation assets needed to operate the system. Ownership terms are defined clearly before build work begins.

How long does a custom AI agent take to build?

A focused internal agent can often be delivered in a few weeks. More complex systems with multiple integrations, regulated data, advanced evaluation, or production handoff can take longer. We usually recommend phased delivery so useful capabilities ship before the full system is complete.

What does a custom AI agent cost?

Cost depends on workflow complexity, integrations, evaluation requirements, deployment posture, and support needs. A simple prototype is different from a production system tied to customer records, clinical workflows, revenue operations, or legal review. CloudNSite scopes work in phases so cost maps to business value.

Can you work with our existing stack?

Yes. CloudNSite builds around the systems you already use, including CRMs, data warehouses, EHR-adjacent systems, internal APIs, ticketing tools, spreadsheets, document stores, auth providers, and cloud infrastructure. Existing stack fit is part of discovery.

Do you build HIPAA-aligned workflows?

Yes. CloudNSite can design HIPAA-Ready Architecture for workflows involving sensitive healthcare data. That means careful attention to hosting, access control, auditability, data handling, vendor boundaries, and operational process. HIPAA compliance is a shared responsibility, so architecture and client operations must work together.

What if we already have a no-code setup we like?

That can be a strong starting point. We can review the existing workflow, identify which parts should stay in no-code, and determine where a custom build would reduce risk or improve reliability. Many good systems combine lightweight automation with custom components.

Is this a good fit for a small team?

It can be, if the workflow is important enough. Small teams often benefit from custom AI automation when the work is repetitive, high value, and hard to hire around. If the need is simple, we will say so and recommend a lighter tool instead.

Architectural fountain plaza for the CloudNSite custom AI build approach and Discovery Sprint.

Home ApproachCustom AI Builds

OUR PROCESS · CUSTOM AI DEVELOPMENT

Custom AI development,built for real impact.

CloudNSite is the AI development team behind the workflow. We scope, build, evaluate, deploy, and operate custom AI agents and AI workflow automation inside the systems your team already runs. No platform tax. No rip-and-replace. One accountable team for the lifetime of the build.

Book a Discovery Sprint Talk to the Build Team

How we build

Six steps from workflow map to operating system.

Scope

Confirm the workflow, success metrics, users, integrations, and boundaries.

Build

Develop the custom AI agents, automations, prompts, integrations, and interfaces.

Evaluate

Test outputs, edge cases, handoffs, permissions, and failure paths.

Deploy

Launch into the real environment with the right access controls and monitoring.

Train & Handoff

Document the system and train the people who will use or manage it.

Iterate

Refine based on usage, feedback, performance, and new workflow requirements.

Discovery sprint

Discovery is paid, scoped, and yours to keep.

We cover

The workflow, department, or process you want to improve
Where work slows down, gets duplicated, or depends on manual handoffs
The systems, data sources, and approvals involved
Whether the opportunity is better suited for no-code, low-code, vertical SaaS, or custom AI development
The clearest next step: no fit, Pilot, Discovery Sprint, or Production planning

We do

Stakeholder interviews
Workflow mapping
System and data handoff review
Bottleneck analysis
Security and access requirements review
Automation opportunity ranking
Rollout order planning
Implementation plan development

You walk away with

Department workflow map
Pain point and bottleneck summary
Systems, handoffs, and dependencies overview
Prioritized automation roadmap
Implementation scope document
Estimated ROI and time-savings summary

What gets shipped

Production deliverables, not slide decks.

Each custom AI development engagement ships the implementation assets your operators, technical team, and compliance stakeholders need to run the system.

Workflow and systems map
Agent architecture and technical scope
Production source code
Prompt, tool, and retrieval configuration
Evaluation suite with representative test cases
Integration with existing business systems
Human review and escalation paths
Deployment runbook and operating documentation
Monitoring, logging, and failure handling guidance
Handoff session or retained operations plan

When each fits

When custom AI is the right call.

We help teams choose between no-code, low-code, vertical SaaS, and an owned custom build based on risk, workflow depth, and operating value.

When no-code is the right call

Use no-code when the workflow is simple, low risk, and already matches the connector model. If you need to move form submissions into a CRM, send alerts, create tasks, or test a workflow idea quickly, no-code can be the best fit. It is also useful before a custom build, because it can prove that a workflow is worth automating.

When low-code agent platforms are the right call

Use low-code agent platforms when you want more flexibility than simple triggers but still need speed. They can be a good fit for outbound research, inbox assistance, lightweight sales tasks, browser actions, enrichment, and internal assistant workflows. They are strongest when the process can live inside the platform's constraints.

When vertical SaaS is the right call

Use vertical SaaS when the workflow is common, mature, and well served by an existing product. If your team needs standard scheduling, ticketing, CRM automation, support routing, revenue intelligence, or document management, a proven product may be the best fit. The tradeoff is that your process must adapt to the product.

When CloudNSite custom build is the right call

Use CloudNSite when the workflow is important enough to own. Custom AI solutions make sense when the system must connect deeply to your stack, respect specific data boundaries, support custom business rules, handle exceptions, and be evaluated before launch. This is where custom AI agents versus no-code becomes a business decision, not a tooling preference.

Beyond launch

Managed Ops keeps the system aligned with the business.

Managed Ops includes

Monthly optimization review
Shared roadmap
Tweaks and workflow changes
New automation opportunity identification
Monitoring and support
Expansion planning

Partnership signals

Named implementation lead, not a rotating account team
Shared Slack or Teams channel during active work
Weekly build updates during implementation
Monthly optimization review after launch
Shared automation roadmap with prioritized backlog
Clear ownership of security, access, and approvals
ROI and time-savings review tied to baseline metrics

Built on principles, not hype.

24/7

Build team availability

99.9%

Production uptime SLO

200+

Implementations shipped

<6w

First production AI ship

Frequently asked

Custom AI development, questions answered.

Straight answers on Discovery Sprint scope, private AI, HIPAA compliant AI, ownership, pricing, AI automation services, and what happens after launch.

Bring us the workflow that costs you time every week.

Bring the process, systems, and constraints. CloudNSite will scope the Discovery Sprint, define the AI implementation path, and show where production AI agents can create measurable operating leverage.