Your Systems Are Already Doing Half This Work. AI Can Do the Rest.

Beyond using AI tools, this is putting AI to work inside your business. We build agentic workflows that connect to the systems your team already uses and automate the processes they are still running manually.

 

$3K

per month down to

$50

per month. Running in production.

Data Adjudication System Replacement

A client in the data aggregation and adjudication space was running a purpose-built legacy system to handle their core processing work. The system cost over $3,000 a month to operate and required ongoing developer involvement for any change to the logic.

ManagePoint replaced it with a multi-agent AI architecture. Multiple agents work in sequence: ingesting source data, executing the adjudication logic, producing baseline analysis outputs, and formatting results for delivery. The system runs on current general AI platforms with no proprietary infrastructure and no vendor lock-in.

Monthly operating cost dropped to under $50. The client’s team maintains the logic themselves. When the rules change, they update the workflow. No ticket raised, no developer involved, no wait.

That outcome is not luck. It is the result of architecture decisions made before a line of code was written.

What “Built Properly” Actually Means

A working agentic workflow is not a single AI call with a prompt attached to it. It is a system of decisions about memory, context, retrieval, validation, and handoff that has to be designed deliberately. The difference between a workflow that runs for three years and one that breaks in three months sits in these decisions.

Memory and context

Agents need to know enough to do their job and not so much that costs spiral. We design what each agent remembers, what gets passed forward, and what gets discarded. Without this discipline, token costs grow until the system becomes too expensive to run.

Context compression

Newer techniques let us compress what an agent carries forward without losing the substance of it. For workflows that run at volume, this is the difference between a $50 monthly bill and a $1,500 one for the same outputs.

Hallucination guardrails

Every workflow we build assumes the AI will, at some point, produce a confident wrong answer. We design validation steps, source grounding, and escalation logic that catches these before they reach a customer or a downstream system.

Retrieval that finds the right thing

When a workflow needs to pull from your documentation, knowledge base, or historical records, the retrieval layer is where it succeeds or fails. We tune retrieval to your actual content, not to a generic template.

Audit and traceability

When something goes wrong, you need to be able to see what the agent did, what it saw, and why it made the decision it made. We build with logging that lets your team trace any output back to its source.

These are the things that turn a demo into a production system. They are also the things that get skipped when someone is building fast or building without the underlying understanding.

What We Build

Every workflow we build starts with a thorough understanding of the process it is replacing or augmenting. These are the categories we work in most often.

Multi-Agent Data Pipelines

Multiple AI agents working in sequence to ingest, process, validate, and output data. Replaces manual review chains and legacy processing systems.

Integrating Data Sources

Most AI workflows fail because they cannot reach the data they need. We connect AI to your CRMs, databases, analytics platforms, file stores, and third-party APIs so workflows can read and write where your business actually lives.

Task Aggregation

A platform-agnostic layer that sits across your CRM, project management tools, and personal workspace to surface everything outstanding in one view.

Document Processing

Extraction, classification, formatting, and routing of documents at volume. Replaces manual document handling without sacrificing accuracy or auditability.

Internal Knowledge RAG

Retrieval-augmented generation systems that let staff query internal documentation in plain language. Faster onboarding, fewer interruptions to senior staff.

Custom Integrations

When the off-the-shelf connectors do not go far enough. We build direct API integrations so AI workflows read and write to the systems that actually run your business.

Built to Hand Off

Every workflow we deliver is designed to be owned and operated by your team after we hand it off. No ongoing developer dependency, no black box.

The reason that handoff actually works is that the system was designed for it from the start. Clear agent roles. Documented data flows. Logic written in a form your team can read and modify. Validation steps that surface problems in plain language. The $3K to $50 client maintains their adjudication logic themselves because the system was structured to let them.

01

Process Audit

We map the process as it actually runs before deciding what AI should touch.

02

Workflow Design

Agent roles, data flows, error handling, and integration points are defined before we write a line of code.

03

Build and Test

Built against real data, tested against edge cases, and validated against the original process outputs before deployment.

04

Handoff and Documentation

Your team receives documentation, a working knowledge transfer, and direct access to the people who built it. They maintain and extend without coming back to us.

Common Questions

Answers to what we hear most often from businesses looking at agentic AI for the first time.

Basic automation follows a fixed script. If something unexpected happens, it fails or stalls. An agentic workflow uses AI agents that can reason through a task, make decisions based on the data they encounter, and adapt when conditions change. Where a traditional automation might stop if a document comes in with an unusual format, an agent can figure out what to do with it and keep moving. The practical difference is that agentic systems handle the messy, variable work that scripted automation cannot.

No. Most of the workflows we build connect directly to the tools you already use. HubSpot, Microsoft 365, Google Workspace, your CRM, your accounting software. We build integrations to these rather than asking you to move off them. In most cases your team keeps working in the same tools they know, and the AI handles what happens behind the scenes.

It depends on the complexity of the process and how many systems are involved. A focused single-process workflow can go from scoping to deployment in two to four weeks. A multi-agent system that spans several tools and handles exception logic will take longer. We scope every engagement individually and give you a timeline before work starts, not after.

We design for it. Every workflow we build includes validation steps, error handling logic, and clear escalation paths for cases the AI should not handle on its own. We also build against real data during testing rather than synthetic examples, so the edge cases that exist in your actual operation get addressed before deployment, not after.

Cost control is an architecture decision, not a monitoring decision. We design what each agent needs to know, what context gets passed where, and how information is compressed between steps. A workflow that handles the same volume of work can cost ten times more or ten times less depending on those decisions. We build with cost as a first-class constraint.

That is the goal. We build for handoff from day one. The documentation we provide covers how the workflow is structured, what each step does, and how to make common changes without developer involvement. The $3K to $50 client we mention on this page manages their own system. When the adjudication rules change, they update the workflow themselves. That is the standard we hold ourselves to.

Tell us the process. We will tell you if AI can take it over.

Most businesses have at least one process that is eating hours every week and has never been looked at as an automation candidate. Book a call and walk us through it. We will be direct about whether it is a good fit and what building it would actually involve.

Platform-agnostic builds, no vendor lock-in

Designed for client-side operation after handoff

Built on real software development fundamentals

Microsoft Partner, Claude Partner Network