Paloren designs custom AI agents around your workflow, not a template.
Paloren designs custom AI agents around your workflow, not a template. Compare what a purpose-built agent includes and how it differs from configuring an existing tool.
For companies that need an agent designed around their specific process, data and approval rules.
Paloren designs custom AI agents for workflows that do not fit standard tools. The build includes context, actions, permission boundaries, testing and a named owner, all documented before development starts. Custom agent workflows run from USD 40k to 90k over 6 to 10 weeks.
S4 shapes a custom AI agent around your actual workflow: the systems it reads, the actions it may take, the approvals it must respect. Each stage is documented in the proposal before build starts.
| Stage | What it changes |
|---|---|
| Signal | Map the workflow end to end: process steps, systems involved, data quality, decision points and which actions a configurable product cannot cover. |
| Synthesis | Draft the five design elements, context, actions, boundaries, testing, ownership, plus source contracts naming fields read and feed-failure behaviour. |
| System | Connect each approved system, run the test plan across correctness, permissions, missing data and recovery, and record evidence for every answer. |
| Scale | Name the owner who accepts output, run the support model after launch, and extend the agent one workflow step at a time. |
A claims team commissions an agent to draft settlement recommendations. Signal maps six process steps across a claims system and a policy database, with two approval gates.
Synthesis defines boundaries: the agent may draft but never issue payments. System tests 80 historical claims, including ten with missing policy fields. Scale tracks whether the named claims reviewer accepts drafts without rework while evidence records stay complete.
Written teaching example with hypothetical inputs, not a client result.
Operating metric: Share of agent drafts accepted by the named owner without rework.
What this can change for your team
When the workflow, data or approvals do not fit a standard tool.
How we make this work
A custom AI agent is justified when your process has specific steps, data sources or approval requirements that a configurable product cannot handle without significant compromise.
Paloren assesses this during discovery by mapping the workflow end to end. If an existing tool covers 80 percent of the need and the remaining 20 percent can be handled manually, a custom build may not be worth the cost.
If the process involves proprietary data, regulatory boundaries or a multi-system handoff that no product supports, a custom agent built against your systems is the right approach.
Paloren recommends the smallest build that solves the problem, not the largest one that can be sold.
Context, actions, boundaries, testing and a named owner.
How we make this work
Every custom agent Paloren builds includes the same structural elements. Context: which systems provide the information the agent reads. Actions: what the agent can do, from drafting to updating records.
Boundaries: which actions require a person to accept, and what the agent must never access. Testing: representative cases covering correctness, permissions, missing data and recovery.
Ownership: the named person who accepts the output and the support model after launch. The proposal documents all five elements before build starts, so the scope is clear and comparable against other proposals.
Discovery, source contracts, integration design and a test plan.
How we make this work
Scoping starts with discovery interviews with the people who do the work. Paloren identifies the process steps, the systems involved, the data quality and the decision points.
Source contracts follow: which fields are read, who owns each source and what happens when a feed fails. Integration design maps how the agent connects to each system. The test plan defines representative tasks, edge cases and acceptance criteria.
This sequence prevents misdirected builds and surfaces dependencies, such as API access that needs approving, before they become blockers during delivery.
Scoped permissions, isolation and evidence records.
How we make this work
Custom agents that touch sensitive data need explicit controls. Paloren designs permission-aware retrieval so users only see records their role allows. The agent documents which fields it reads and writes.
Evidence records attach the source of every answer so a reviewer can verify the basis for a recommendation. For regulated industries, Paloren works alongside your compliance team to map controls to existing requirements.
Legal and regulatory interpretation remains with qualified advisers, but the technical controls that make those rules enforceable are part of the build.
Custom agents are built against your data model. Configured tools are adapted to theirs.
How we make this work
A configured tool starts with someone else’s data model and workflow assumptions. You adapt your process to fit. A custom agent starts with your process and builds the connection layer to match.
Paloren builds custom agents when the gap between the tool’s assumptions and your workflow creates risk or manual work. For example, if your approval chain spans three systems and a configured tool only supports one, the manual bridge is where errors happen.
A custom agent removes that gap by design. The trade-off is that custom builds need more upfront scoping and a longer timeline than configuring a product.
Correctness, isolation, recovery and evidence verification.
How we make this work
Custom agents need testing that goes beyond a demo. Paloren runs cases covering correctness, isolation, recovery and evidence. Correctness checks that the output matches the source.
Isolation confirms that a user only sees what their permissions allow. Recovery tests what happens when a source is unavailable or returns an error. Evidence verification confirms that the answer carries its sources.
All required cases must pass before go-live. Changes to sources or permissions trigger re-testing, so the standard is maintained rather than assumed. This is the same testing framework Paloren applies to every production system, whether custom or configured.
From USD 40k to 90k depending on systems, permissions and testing scope.
How we make this work
Custom agent workflows run USD 40k to 90k over 6 to 10 weeks. The cost is driven by the number of connected systems, the complexity of the permission model, whether the agent takes actions or only prepares recommendations, and the depth of testing required.
A custom agent that reads two systems and drafts a summary costs less than one that moves records across four platforms with approval gates. The proposal names deliverables, assumptions, exclusions and acceptance criteria.
Ongoing support is scoped separately, typically from from USD 2,500 per month for 10 hours.
Documentation, training, runbook and a named owner.
How we make this work
The handover pack includes system documentation, evaluation evidence, training materials, the operating model and a named owner. Documentation should be complete enough that a new technical person could understand the system without the original team.
Paloren prepares this as part of delivery rather than as an afterthought. Training prepares the people who will use the agent so adoption does not depend on the build team remaining available.
The runbook describes what to do when something fails, so the team has a process rather than a phone number.
Tell Paloren about the process, the systems and the approvals. Reply from the team within one business day.
Discuss a custom agentInterview the people who do the work and identify the systems involved.
Define context, actions, approvals and prohibited access.
Connect sources, run representative cases and document the operating model.
Train the team and assign monitoring, changes and ownership.
Discovery brief with workflow map
Agent design with permission boundaries
Integration and test plan
Working agent with evaluation evidence
Documentation and training pack
Support and handover model
Custom is justified when your workflow has steps, data or approvals that a configured product cannot support without significant manual work. Paloren assesses this during discovery and recommends the smallest build that solves the problem, which sometimes means configuring an existing tool rather than building from scratch.
Yes. Paloren works with the model provider that fits your requirements and budget. The proposal names which tools are used and why, so the scope is transparent. Sometimes the right answer is to integrate an existing subscription rather than add a new one.
Changes to scope, sources or permissions are documented and re-tested before release. A change request is a normal part of delivery when the team learns something during the build.
The proposal should state how changes are handled, so the process is clear before it is needed.
Paloren designs permission-aware retrieval so users only see records their role allows. The agent documents which fields it reads and writes. Evidence records attach the source of every answer.
Your security team can review the permission model before build starts.
The handover pack includes documentation, evaluation evidence, training materials and the operating model. It should be complete enough that a new technical team could understand the system.
The proposal records what remains available if the engagement ends, so the business knows what it owns and what it depends on.
Paloren builds custom agents for teams in any industry where the workflow is clear and the data can be connected. Paloren combines implementation, workflow design and practical team training.
Ongoing support is optional and priced separately, typically from USD 2,500 per month for 10 hours. The model documents who monitors the system, who approves changes and who handles incidents.
Platform updates, source changes and new requirements are handled through the agreed support scope.
What does your workflow need that a standard tool cannot do?
Discuss a custom agent
