Managed Intelligence Provider · New York & New Jersey
Managed Intelligence Provider for NYC & NJ Businesses
Private AI agents on hardware you own or in Microsoft Azure, grounded in how your business works, and run by one accountable team.
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. One accountable team manages every layer, from the infrastructure to the outcome.
What you get
Managed intelligence, not a one-off AI project
Most AI projects end at go-live. Ours start there. As your Managed Intelligence Provider, Tekscape is accountable for the agents, the systems they run on and the rules they follow.
Private AI, your hardware or Azure
Models run on hardware you own, typically an NVIDIA DGX Spark in your office or rack, or in your Microsoft Azure subscription on Microsoft Foundry. Your data stays under your control, and your costs are easier to predict.
- Hardware sizing and install
- Model serving, patching and monitoring
- Cloud routing only where it pays
A semantic model of your business
We build and maintain an ontology of how your firm works: your clients, matters, contracts, rules and metrics. Agents use your terms, not generic ones.
- Built from systems you already run
- Maintained as your business changes
A managed agent harness
Every agent runs inside a harness we operate: tools, memory, guardrails, human review and audit logs.
- Guardrails per agent and per task
- Human sign-off where it matters
Ongoing optimization
We review cost, quality and usage every month, then tune models, prompts and routing with the aim of lowering cost and raising quality over time.
The stack
Every layer, managed by Tekscape
Several layers sit between where your models run and a business result. We run all of them. You get one team to call, not a separate vendor for each layer.
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.
Your hardware or Azure by default. Other cloud models are used only for work where they are the better deal, and only for data you approve.
Honest economics
Your hardware, Azure or both: we place each job where it belongs
Local AI is not always cheaper. It wins on control and predictability, and on cost when volume or privacy justify it. We size it with your numbers and route the rest to the cloud.
| Workload | Where it runs | Why |
|---|---|---|
| Routine, high-volume tasks | Your hardware | Fixed cost. No per-token bill that grows with use. |
| Sensitive client, patient or financial data | Your hardware | Data stays on-site, under your control. |
| Hard, low-volume reasoning | Cloud model, by approval | Usually cheaper to rent than to buy for occasional use. |
| Businesses already on Microsoft 365 and Azure | Microsoft Azure, on Microsoft Foundry | Runs in the cloud you already govern, with no hardware to own. |
| Everything else | Routed per request | The harness picks the lowest-cost model that meets the agreed quality bar. |
One independent preprint on arXiv estimates on-premises break-even at a few months for small models, about 2 years for medium models and about 5 years for large models. It counts hardware and electricity only, not staffing. 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 is a benchmark between cloud models, not a customer result.
Engagement model
Discover, pilot, run, optimize
We start small, prove value on one workflow, then expand. You judge the pilot on real work before you commit to a managed service.
- Discover
Find the first agent worth building
We map your workflows, data and systems. We pick one high-value, low-risk task and agree how success will be measured.
- Pilot, six weeks
Prove it on your hardware or in Azure
Weeks 1-2: data, ontology and harness setup. Weeks 3-4: build and test the agent with your team. Weeks 5-6: run it on real work under human review, then decide together.
- Run
Operate it as a managed service
The agent goes into production. We monitor it, patch it, review its output and answer for it.
- Optimize
Tune for cost and quality
We tune routing, models and the semantic model. When one agent is paying its way, we add the next.
Packaged offers
Ways to start
Pick the scope that fits. Every offer runs on the same stack, so you can move up without starting over. Pricing is scoped in your free session.
Private AI Pilot
One agent, one workflow, on your hardware or in Azure, over six weeks. Built to test value before you commit.
- Discovery and success criteria
- Hardware sizing
- Go / no-go review at the end
Managed Agent Harness
We run the harness around your agents: tools, memory, guardrails, human review and audit logs.
- Your hardware, Azure or hybrid
- Monitoring and incident response
Managed Ontology
A semantic model of your business, built from what you already run and maintained as you change.
- Shared terms, rules and metrics
- Ongoing upkeep, not a one-time project
Managed Intelligence (full)
The whole stack, end to end: hardware, semantic model, harness, agents and outcomes, from one accountable team.
- Every layer managed by Tekscape
- Monthly cost and quality reviews
Governance & accountability
Agents you can audit, review and hold to a standard
An agent that nobody can inspect is a liability. Governance is built into the harness from day one.
Audit logs
Agent actions are logged: the input, the model used, the tools called and the output. You can trace a result back to its source.
Human review
You decide which actions need sign-off. Agents draft, a person approves. Client-facing work goes through the review you set.
Service levels
Agents run under written service levels for availability, response and escalation, with 24/7 monitoring. You get one accountable team, not a ticket queue.
Data boundaries
Sensitive data stays on your hardware or in your Azure subscription by design. Only the requests you approve are sent to any other cloud model.
Who it's for
Built for New York and New Jersey firms that can't afford to lose control of their data
| Industry | Where agents help | Why private matters |
|---|---|---|
| Finance | Reconciliation, reporting, client onboarding | Client financial data stays on hardware you control. |
| Legal | Intake, document review, matter summaries | Privileged material stays inside your environment. |
| Healthcare | Scheduling, claims prep, patient follow-up | Protected health information can stay on-site. |
| Manufacturing | Quoting, order status, supplier follow-up | Pricing and process knowledge stay in-house. |
| Education | Admissions, student services, reporting | Student records stay under your governance. |
Why Tekscape
Enterprise IT discipline, applied to AI
18+ years of enterprise IT
Our leadership brings 18+ years of enterprise IT experience. We know how to run systems that businesses depend on.
Led by CCIE-certified veterans
Tekscape is led by CCIE-certified veterans. The network, security and infrastructure under your agents get the same rigor as the agents.
Microsoft Cloud Solution Provider
As a Microsoft Cloud Solution Provider, we deploy your agents in Microsoft Azure on Microsoft Foundry when that is where they belong, and connect them to Microsoft 365.
We run on our own agents
Tekscape runs its own operations on the agents and harness we sell. We found the rough edges on ourselves first, not on you.
Frequently asked questions
Sources
- Pax8: The Rise of the Managed Intelligence ProviderPax8's definition of the Managed Intelligence Provider category (blog, October 2025). Credited only; no affiliation implied.
- Pax8: The Agentic Inflection Point (2025 research report announcement)Category origin: Pax8 introduced the Managed Intelligence Provider term in June 2025. Credited only; no affiliation implied.
- arXiv 2509.18101: on-premises LLM deployment cost and break-evenPreprint, not peer reviewed. Counts GPU cost and electricity only; staffing and maintenance excluded. As of 2026-09-29.
- LMSYS: RouteLLMBenchmark of routing between API models, not a local-hardware or customer result. As of 2026-09-29.
Find your first agent in a free session
Meet with our team for a free session. We'll map one workflow, check whether local, cloud or hybrid fits it, and tell you plainly whether a pilot is worth it.
Book a free session