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Managed Intelligence Provider (MIP) for New York and New Jersey

Managed intelligence, on your hardware or in Azure.

We deploy, govern and run private AI agents where you choose: on hardware you own, typically an NVIDIA DGX Spark, or in Microsoft Azure on Microsoft Foundry. Either way they are grounded in a semantic model of your business. You keep your data, and you can predict your costs.

Tekscape is a Managed Intelligence Provider (MIP) for businesses in New York and New Jersey. We deploy, govern and run AI agents on hardware you own or in Microsoft Azure, grounded in a semantic model of your business. It is private AI, run as an accountable managed service, not a one-off project.

Compact AI server hardware on an office shelf at dusk, lit in navy, cyan and gold

The shift

From managed IT to managed intelligence

For years your provider kept the lights on. Now the job is running AI agents that do real work, and being accountable for what they do.

  1. Then

    Managed Service Provider (MSP)

    Keeps devices, networks and security running. Success means fewer tickets and less downtime.

  2. Transition

    MSP with AI tools

    Adds AI licenses and chat assistants. People still do the work. The AI helps at the edges.

  3. Now

    Managed Intelligence Provider (MIP)

    Deploys, governs and runs AI agents that take on real workflows, and manages the outcomes. Ours run privately, on hardware you own or in your Microsoft Azure subscription.

Pax8 introduced the Managed Intelligence Provider term. Tekscape's version adds a choice of where it runs (your hardware or Microsoft Azure), a business ontology and hybrid routing.

What we run

Four pillars of private managed intelligence

Each pillar is a managed service. Together they make up the stack we run for you.

  • Managed Intelligence

    We deploy, govern and run AI agents as an accountable managed service, not a one-off project.

    • One team, accountable for how the agents perform
    • Ongoing tuning, not a handoff

  • Private AI, Your Hardware or Azure

    Run models on hardware you own, typically an NVIDIA DGX Spark, or in your Microsoft Azure subscription on Microsoft Foundry. We manage either.

    • Sizing, install and model serving
    • Monitoring and patching included

  • Business Ontology & Semantic Model

    A managed model of how your business works, so agents use your terms, rules and metrics.

    • Built from the systems you already run
    • Maintained as your business changes

  • Managed Agent Harness

    Tools, memory, guardrails, human review and audit logs around every agent we run.

    • Agent = model + harness
    • Every action logged and reviewable

Why private

Your hardware or Microsoft Azure: you choose where it runs

Some businesses want the models in their own building. Others want them in the cloud they already run. We deploy on hardware you own, typically an NVIDIA DGX Spark, though we work with the hardware you choose, or in your Microsoft Azure subscription on Microsoft Foundry.

Running AI on your own hardware isn't always cheaper. What it buys is control and predictability. When the cloud is the better fit for a task, we run it there.

  • Control

    On your own hardware, you own the machine and the models on it, so no provider can reprice, retire or change the terms of those models.

    • Your models, your version, your schedule

  • Predictable cost

    Hardware is a known cost, not a meter that runs with every prompt. You can budget for it like any other asset.

    • Fixed capacity instead of per-token surprises

  • Data stays where you put it

    On your hardware, sensitive work stays in your building. In Azure, it stays in your own Azure subscription, under your access rules. You choose what, if anything, goes out to a public AI model.

    • A documented data boundary you can review

  • Hybrid routing, honestly

    Routine and sensitive work runs where you deployed it, on your hardware or in your Azure subscription. Hard reasoning can go to a frontier cloud model when that costs less or works better. In the RouteLLM benchmark from LMSYS, routing between GPT-4 and a cheaper model cut cost by over 85% on MT Bench while keeping 95% of GPT-4 quality. That was a benchmark between API models, not a local-hardware or customer result.

    • Your deployment by default, frontier models when they pay

The stack

One stack, managed end to end

It starts at the bottom with where the models run: hardware you own, typically an NVIDIA DGX Spark, or your Microsoft Azure subscription. On top of that sits your data and a semantic model that describes your clients, contracts, invoices and tickets the way your business actually uses them.

Above that is the managed agent harness: tools, memory, guardrails, human review and audit logs. The agents run inside it. At the top are the outcomes you measure.

Tekscape manages every layer. A routing layer sends a task to a cloud model only when that is the better deal.

Your hardware or Microsoft Azure

Hardware you own, typically an NVIDIA DGX Spark, or your Azure subscription on Microsoft Foundry. Sized, monitored and patched.

Data + semantic model / ontology

Your systems mapped to shared terms, rules and metrics.

Agent harness: tools, guardrails, evals, audit

What each agent may touch, what it must check, and a log of what it did.

AI agents

Task-specific agents for finance, service and operations work.

Business outcomes

Work finished and measured against the metrics you already track.

Managed by Tekscape
Cloud models (routed when cheaper) Frontier models for the hard cases, reached through the harness policy gate.
Read from the bottom up. Tekscape runs the bottom four layers; the outcomes are yours.

Cost, honestly

Is local AI cheaper for you? Run your own numbers.

The honest answer depends on your volume, your model size and how fast cloud prices keep falling. We won't promise savings before we've seen your numbers.

Enter your token volume, API price and hardware cost in the AI cost calculator to see when local AI pays back. Falling cloud API prices are modeled and every assumption is shown. Then read the local vs cloud cost guide for the evidence, the counter-arguments and where hybrid routing wins.

Customer Zero

We run on what we sell

No standard or certification exists yet for a Managed Intelligence Provider. So the proof has to be operational.

Before we run agents for clients, we run them on ourselves. Tekscape runs its own finance, vendor and service workflows on the same managed intelligence stack we deploy for clients: private hardware, a semantic model of our business, and a harness with guardrails and human review.

Pax8's Managed Intelligence Provider Playbook calls this idea Customer Zero. It is why we can tell you what works, what breaks, and what needs a person in the loop.

The foundation

Managed IT is still the foundation

Agents are only as reliable as the systems under them. Our managed IT, cybersecurity, cloud, Microsoft Copilot and collaboration services are still available, on their own or as the base your private AI runs on.

  • Managed IT services

    Support, networks and 24/7 monitoring for New York and New Jersey offices.

  • Cybersecurity

    Managed security, compliance audits, awareness training and incident response.

  • Cloud

    Azure, Microsoft 365, disaster recovery, and putting each workload where it runs best.

  • Microsoft Copilot

    The cloud option inside a hybrid setup, set up and governed alongside your private models.

  • Collaboration

    Teams calling, UCaaS and video conferencing.

How we engage

From first conversation to a running agent

Start small, prove it with your data, then expand. Our pilot-to-production path is planned around six weeks, followed by ongoing management.

  1. Weeks 1-2

    Discover

    We map one workflow worth automating, the data it touches and the rules it follows. Then we size the hardware or Azure deployment, and the routing, against your volume and data requirements.

  2. Weeks 3-4

    Pilot

    We build the agent inside the managed harness, grounded in a first slice of your semantic model, with human review turned on.

  3. Weeks 5-6, then ongoing

    Run & optimize

    The agent goes to production on hardware you own or in Microsoft Azure. We monitor it, tune it, extend the semantic model and adjust routing as volumes and prices change.

Customer Zero, in practice

The agents we run, for clients and for ourselves

Every agent below runs inside the same managed harness, with human review before anything goes out. Client work is anonymized.

Agents we build for clients

Production agents deployed into real operations — not demos. Every build connects to existing systems and ships on the six-week pilot-to-production path.

Workflow automation

Document & Email Intake Agents

Read inbound documents and emails, extract the structured fields that matter, validate them against your business rules, route each record to the right system, and notify the team in real time.

Learn more →
Operations

Process Orchestration & Observability Agents

Wrap opaque or legacy processes with a layer of monitoring — detect failures, tag work by downstream impact, and surface exceptions before they cascade into bigger problems.

Learn more →
Financial operations

Billing & Revenue Reconciliation Agents

Reconcile invoices, contracts, and payments across multiple systems to surface discrepancies and revenue leakage that manual review routinely misses.

Learn more →
Service delivery

Reporting & Scorecard Agents

Turn live operational data into multi-format dashboards, scorecards, and executive reports — generated on demand instead of assembled by hand.

Learn more →

Agents we run on our own operations

Before we deploy a single agent for a client, we run it on ourselves. Tekscape’s own operations are powered by production agents across every department.

FAQ

Frequently asked questions

What is a Managed Intelligence Provider?
A Managed Intelligence Provider (MIP) deploys, governs and runs AI agents for a business as an ongoing managed service, and is accountable for the outcomes. Pax8 introduced the term. Pax8 describes MIPs as providers that deploy and manage intelligent systems with AI agents, going beyond infrastructure support and ticket resolution. No independent certification or standard exists for the label. Tekscape's version is private: agents run on hardware you own or in Microsoft Azure and are grounded in a semantic model of your business.
Is local AI actually cheaper?
Sometimes, not always. It depends on your volume and model size. One independent arXiv preprint puts on-prem break-even within a few months for small models, about two years for medium models and about five years for large ones. It finds on-prem viable mainly at roughly 50M+ tokens a month or under strict data-residency rules. That paper counts GPU and electricity costs only, not staffing. a16z also estimates that API prices for equivalent models fall about 10x per year. That is why we route: local where it pays or where privacy requires it, cloud where it doesn't. The calculator runs your own numbers.
Do we have to buy hardware?
No. If you want the models on-site, we size hardware during discovery against your actual volume, model needs and data requirements. That is typically an NVIDIA DGX Spark, but we are not tied to one vendor. If you would rather not own hardware, we run the same agents in your Microsoft Azure subscription on Microsoft Foundry. You make the decision with a break-even in hand.
Do you still do managed IT?
Yes. Managed IT, cybersecurity, cloud, Microsoft Copilot and collaboration services are all still available. You can buy them on their own. They are also the foundation your private AI runs on, since agents are only as reliable as the systems under them.
Why do AI agents need a semantic model of our business?
Because agents give better answers when they understand your business terms, rules and metrics, not just your raw tables. In one published benchmark, GPT-4 zero-shot question answering over an enterprise SQL schema scored 16%. Over a knowledge-graph representation of the same database it scored 54%. That paper had vendor-affiliated authors and covered the insurance domain only. We build the semantic model from the systems you already run and maintain it as a managed service.
Where does our data go?
Where you choose to put it. On hardware you own, sensitive work stays on-site by default. In Microsoft Azure, it stays in your own Azure subscription, under your access rules. When a task is routed to another cloud model, you decide in advance which kinds of prompts may leave. The harness logs every call so you can audit it.

Sources

  1. Pax8: The Rise of the Managed Intelligence ProviderPax8's definition of the Managed Intelligence Provider category (blog, October 2025)
  2. Pax8: The Managed Intelligence Provider PlaybookSource of the Customer Zero idea
  3. CIT Solutions: What is a Managed Intelligence Provider?No independent certification or standard exists for the label
  4. arXiv preprint: on-prem vs cloud LLM break-evenPreprint, not peer reviewed; counts GPU and electricity only, staffing excluded
  5. a16z: LLMflationAPI prices for equivalent capability fall about 10x per year
  6. LMSYS: RouteLLMBenchmark of routing between API models, not a local-hardware or customer result
  7. arXiv: knowledge graphs and LLM question answering over enterprise SQLBenchmark by vendor-affiliated authors, insurance domain only

Find your first private AI agent

Bring one workflow that eats your team's time. We'll map it, size the hardware or Azure deployment and the routing, and tell you honestly whether it pays for itself.

Book a free AI outcomes session