AI agent development, New York and New Jersey
AI agent development for New York businesses
We design, build and run AI agents that do real work inside your business: reconciling, triaging, drafting and monitoring. Then we stay accountable for them, the way a managed service provider stays accountable for your network.
Tekscape is an AI agent development company based in New York. We build production AI agents for mid-market businesses in New York and New Jersey and then run them as a managed service, on hardware you own or in your own Microsoft Azure tenant. Each agent is grounded in a semantic model of your business and works inside a managed harness with human approval, audit trails and monitoring.
What we build
Agents that do work, not chat windows
Back-office agents
Reconcile invoices against contracts, match ledger and billing records, and flag breaks for a person to resolve.
Service and operations agents
Read incoming tickets and requests, add context, route them to the right queue and draft the first response for review.
Briefing and reporting agents
Assemble daily or weekly briefs from your systems, with every figure traced back to its source record.
Monitoring agents
Watch configuration drift, expiring credentials and failed jobs, and raise an alert before a small problem becomes an outage.
How we build
From first workflow to production in six weeks
We start with one workflow that matters, prove it on real work, and only then widen the scope.
- Weeks 1-2
Discover and design
Pick one high-value workflow, map the systems and data it touches, and agree what the agent may and may not do and the tests it must pass.
- Weeks 3-4
Build the agent and its harness
Connect the agent to your systems with least-privilege access, build the guardrails and review steps, and test it with your team.
- Weeks 5-6
Run it on real work
The agent runs on live work under human approval. We measure accuracy and time saved, fix what fails, and hand you a production runbook.
Why a managed provider
Build-and-leave versus build-and-run
| Question | Typical one-off build | Tekscape |
|---|---|---|
| Who runs the agent after launch? | Your team, or nobody | We do, as a managed service with 24/7 monitoring |
| Where does your data go? | Usually a third-party cloud API | Hardware you own or your own Azure tenant |
| What happens when a model changes? | A new project | We re-test and switch models inside the harness |
| Who approves actions outside the company? | Often unclear | A named person, every time, with an audit trail |
| Who supports the rest of your IT? | Someone else | The same team: managed IT, security and cloud |
Cost
Keeping agents affordable to run
Most of an agent's lifetime cost is running it, not building it. We route each task to the cheapest model that meets the quality bar. In LMSYS's RouteLLM study, routing between a strong and a cheaper model cut cost by over 85% while keeping 95% of GPT-4 quality on the MT Bench benchmark.
For steady, high-volume work, running models on hardware you own can pay back: one preprint estimates break-even within a few months for small models and about 2 years for medium models, counting GPU cost and electricity only. Our cost calculator lets you test your own numbers.
Frequently asked questions
Sources
- LangChain: The Anatomy of an Agent HarnessAgent = Model + Harness.
- LMSYS: RouteLLMRouting between a strong and a cheaper model cut cost by over 85% while keeping 95% of GPT-4 quality on MT Bench.
- arXiv preprint: on-prem vs cloud LLM break-evenPreprint, not peer reviewed; counts GPU cost and electricity only.
- Pax8: The Rise of the Managed Intelligence ProviderPax8 defines the Managed Intelligence Provider category.
Pick the first workflow
Bring one process that eats your team's time. We will show you what an agent for it would do, where a person stays in control, and what it would take to run.
Book a free AI outcomes session