Built by Developers Who Understand Both the Machine and the Business Problem.

ManagePoint is a software development and machine learning firm. We build production-grade AI systems for organizations that have moved past the experimentation phase.

A Development Company First

ManagePoint was built on software development and machine learning expertise. Before AI became a marketing term, we were building systems that required understanding both the technical architecture underneath and the business logic it needed to serve.

That foundation matters when you are building something that needs to run reliably in production, integrate with infrastructure that already exists, handle real data at real volumes, and be maintained by real people over time. A system that works in a demo and falls apart in practice is not a system — it is a proof of concept that someone sold as a deliverable.

What we build runs. What we hand off, you can own.

01

PHP, Python.NET, Ruby, Node.js

02

ML pipelines, LLM orchestration

03

Cloud infrastructure and deployment

04

API design and integration

05

Production-grade testing and documentation

CASE STUDY: WHAT PRODUCTION-GRADE LOOKS LIKE

$3,000 per month down to under $50 per month.
Running in production.
Maintained by the client without a developer.

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 the product of building the right architecture for the right problem.

What We Build

For teams that have moved past the question of whether AI is worth pursuing and are ready to build something that actually runs.

Multi-Agent Systems

Multiple AI agents orchestrated to work in sequence or in parallel. Designed for complex business logic that a single model cannot reliably handle alone.

RAG Systems

Retrieval-augmented generation systems that connect AI to your internal documentation, knowledge bases, or proprietary data. Accurate, sourced, auditable outputs.

Custom Integrations and API Development

When AI needs to read and write to the systems that actually run your business. We build the integrations that connect AI workflows to real infrastructure.

Legacy System Replacement

Purpose-built systems replaced with AI architectures that are cheaper to run, easier to maintain, and not locked to proprietary infrastructure.

MCP Server Development

Model Context Protocol is the standard that lets AI systems connect to external tools, databases, and APIs in a structured, maintainable way. We build custom MCP servers for your internal platforms so your AI systems have authoritative, real-time access to the data they need to act on – not stale exports, not brittle workarounds.

MCP Integration and Deployment

Beyond building servers, we configure and deploy the MCP ecosystem your workflows depend on. HubSpot, Microsoft 365, Google Workspace, internal databases, proprietary systems – we connect them to your AI stack and manage the integration layer so your team does not have to.

How We Approach a Build

Every engagement starts with understanding the problem before touching the technology.

01

Discovery

Deep understanding of the business problem, existing systems, data, and constraints before any technical decisions are made.

02

Architecture and Design

System design with long-term maintainability in mind. Agent structure, data flows, integration points, and error handling defined before build begins.

03

Build and Test

Built against real data, tested for edge cases, and validated against the original system’s outputs before deployment to production.

04

Handoff and Documentation

Full documentation and knowledge transfer so your team owns what was built and can extend it without coming back to us for every change.

Common Questions

Questions we get from organizations that have moved past early AI experimentation and are evaluating production-grade builds.

A multi-agent system uses multiple AI models working in coordination, each handling a specific part of a larger task. You need one when the work is too complex or too variable for a single model to handle reliably – when there are distinct stages that benefit from specialized handling, when quality needs to be checked at intermediate steps, or when the volume and variety of inputs require parallel processing. For straightforward, repeatable tasks, a single well-designed workflow is usually enough. Multi-agent architecture is for the harder problems.

Retrieval-augmented generation connects an AI model to a structured knowledge base so it can retrieve relevant information before generating a response, rather than relying only on what it was trained on. The difference from just giving the AI your documents is precision and scale. A RAG system can search across thousands of documents in milliseconds, pull only the relevant sections, and ground its response in those specific sources. It also means the knowledge base can be updated without retraining the model – you add documents and the system can use them immediately.

We design with PIPEDA compliance as a baseline for all Canadian client work. That means understanding where data lives, how it moves, who can access it, and what the retention and consent requirements are before any system is built. We document the data flows as part of the architecture and can work within your existing security and compliance framework. For sensitive industries we can discuss on-premise deployment or air-gapped configurations depending on what the situation requires.

Yes, and it is one of the more common engagements we take on. The process starts with understanding what the system actually does – which is often more nuanced than the documentation suggests – and then designing an AI-based replacement that matches the outputs while removing the proprietary infrastructure and developer dependency. The data adjudication system we reference throughout this site is an example of that. The goal is always to replicate the outputs your business depends on while dramatically reducing what it costs to run and maintain.

We start with a scoping call to understand the problem, the data environment, and what success looks like. From there we produce a written scope with timeline and cost before any work begins. The build phase involves architecture design, development against your actual data, staged testing, and a structured handoff including documentation. For most production-grade builds you should expect four to ten weeks depending on complexity. We stay available after deployment for questions during the first operational period.

Bring us the hard problem.

If you have a system that needs replacing, a process that is too complex for off-the-shelf tools, or an AI build that stalled with another provider, we want to hear about it. We scope honestly and build properly.

Multi-agent and RAG architecture

PIPEDA-compliant by design for Canadian businesses

Legacy system replacement with full documentation

Scoped in writing before work begins