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Manage Your AI Agents and Workflows From a Custom Filament Dashboard

Connecting OpenAI, Claude, or DeepSeek to your business is the easy part. Managing what happens next — prompt versions, cost per request, which outputs need human review before they go anywhere — is where most AI integrations quietly fall apart. We build internal dashboards that give your team direct control over every AI-powered workflow running in your business, instead of a black box nobody can inspect when something goes wrong.

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Full VisibilityPrompt Versions & Cost Tracking

Provider-AgnosticOpenAI, Claude & DeepSeek Support

Human-in-the-LoopReview Queues Built In

2-3 wksTypical Build Time

Why AI Features Bolted Onto Generic Tools Become Unmanageable

AI features usually get added directly into application code, one at a time, with no central place to see which prompts are actually running in production, what each one costs per call, or how their output quality is trending. The moment a business has more than one AI-powered feature, nobody has a single view across all of them — just scattered code paths each doing their own thing.

Prompts get edited directly in code and redeployed every time they need adjusting, with no version history and no easy way to compare an old version against a new one. When an AI output quietly degrades in quality after a tweak, there's no record of exactly what changed or a safe way to roll back to the version that was working correctly the week before.

AI-generated output that genuinely should be reviewed before it reaches a customer — a drafted email, an automated pricing decision, a generated report — often has no defined human checkpoint built in. It either ships completely unreviewed, or someone has to manually dig through application logs to figure out what actually needs a second look, which defeats the purpose of having a review step at all.

No visibility into API cost broken down by feature or by customer, so a usage spike is only discovered when the monthly bill arrives.

Locked into a single AI provider with no straightforward way to compare output quality or switch if a better or cheaper model becomes available.

If the AI capability itself — not just the dashboard managing it — needs to be engineered from scratch, our LLM integration work covers the underlying model integration this panel sits on top of.

Core Modules of Your AI Automation Dashboard

Prompt Library & Version Control

Every prompt versioned, tested against real examples, and rolled back safely if a change degrades output quality — not edited live inside application code with no history.

Multi-Provider AI Configuration

Route different tasks to OpenAI, Claude, or DeepSeek from a single panel, and switch providers based on cost or quality without a code deploy.

Cost & Usage Monitoring

Real-time token usage and cost tracking broken down per feature, per team, or per customer — so a usage spike is visible the day it happens, not the day the invoice arrives.

Human Review Queue

AI-generated output routed to a review screen before it ships anywhere, with approve, edit, and reject actions built directly into the panel your team already works in.

Agent & Workflow Orchestration

Chain multiple AI steps together — extract, classify, generate — and monitor the status of each stage individually, instead of one opaque process with no visibility into where it stalled.

Structured Output Validation

AI responses that fail to match your expected data structure get flagged and surfaced immediately, instead of silently breaking whatever downstream system was expecting clean data.

Knowledge Base & RAG Source Management

Manage which documents feed your retrieval pipeline directly from the panel — add, remove, or re-index sources without a developer, built on the same RAG architecture we implement for knowledge base projects.

Audit Log for Every AI Decision

A complete history of what the AI generated, when, and what a human did with it afterward — the record you need for debugging, compliance, or simply understanding what happened.

Why a Custom Panel Beats Managing AI Through Code Alone

Non-Technical Team Members Can Review Output

Anyone on the team can review, approve, or flag AI-generated content through a normal interface — no reading application logs or asking a developer to check something.

One Place to See Cost Across Every Feature

Total AI spend, broken down by where it's actually going, instead of a single opaque line item on a provider's monthly invoice.

Switch or Add Providers Without a Redeploy

Provider and model selection lives in configuration, not hardcoded in application logic — changing your AI stack becomes a panel setting, not an engineering project.

Full Audit Trail for Compliance and Debugging

Every AI decision logged and traceable, which matters both for regulatory questions and for the ordinary work of figuring out why an output looked wrong last Tuesday.

Custom AI Dashboard vs. Managing AI Directly in Code

Features	Managing AI Directly in Code	Custom Filament AI Dashboard
Prompt version history with safe rollback
Cost visibility broken down per feature or customer
Human review queue for AI output before it ships
Switch AI providers without a code deploy
Zero setup time for a first, simple AI call
No panel to build or maintain for a one-off script

How We Build Your AI Automation Dashboard

Discovery Call

A free call to understand which AI-powered workflows already exist in your business, which providers you're using, and where visibility or review is currently missing.

Scoping Document

Within three days, a written scope covering prompt management, provider configuration, review workflows, and cost tracking — with a fixed price.

Two-Week Build Sprints

A working, testable panel delivered at the end of each sprint, tested against real AI calls and real cost data, not a mocked demo.

Launch & Handoff

Your panel goes live with your team trained on the review workflow directly. Full documentation delivered, plus thirty days of free bug coverage.

Frequently Asked Questions About AI Automation Dashboards

Both, typically. The dashboard is the management layer; the underlying model integration — prompt engineering, structured output, provider fallback logic — is scoped as part of our LLM integration work. Many projects need both built together.

Yes. If the AI dashboard needs to trigger or feed into broader business processes, that's covered by our AI workflow automation work — the dashboard becomes the control panel for automation running across your other systems.

Yes — that's the specific problem this solves. Reviewers see AI-generated output in a normal interface with approve, edit, and reject actions, without needing to read code or application logs to understand what happened.

Yes, if your setup includes retrieval-based AI. Source management is built on the same RAG architecture we use for standalone knowledge base projects, exposed directly inside your panel.

Real-time cost tracking per feature and per customer, plus routing logic that sends simpler tasks to cheaper models rather than defaulting everything to your most expensive option — visibility and control built in from the start, not discovered after an invoice.

A panel covering prompt management, single-provider configuration, and a basic review queue typically takes two to three weeks. Projects with multi-provider routing, workflow orchestration, and RAG source management run four to six weeks, fixed sprint by sprint in the scoping document.

Stop Managing AI as a Black Box Nobody Can Inspect

Get direct visibility and control over every AI-powered workflow running in your business, in a panel your team already knows how to use.