Vectrel

Custom Agentic
Infrastructure

We build AI systems around the infrastructure, data boundaries, and operating constraints approved for the engagement, focused on high-value workflows.

GOVERNED • AUDITED • HUMAN-IN-THE-LOOP

For PE-Backed companies, For Mid-Market, For Enterprise
Start a projectSee our work

Platforms and models, selected per engagement

OpenAISnowflakeMicrosoft AzureAmazon BedrockVertex AI

Services

End-to-end AI, from strategy to production. Seven services that work on their own or together. Each one is scoped to the systems, data, and vendors in play, and to the responsibilities we set before work begins.

Not sure where to start?

Tell us about the workflow; we’ll tell you what we’d build.

Start a project

Selected Work

What changed after the system shipped.

Approved qualitative evidence, published anonymized on the client's terms. Client labels are generalized in this release.

See our work

PE-Backed National Legal Services Firm · Legal

Automating UCC Document Extraction and Classification

Before
Staff pulled each UCC filing from the state registry and keyed its details by hand, and filing volume was climbing faster than the team could review.
System
A pipeline searches the state registry, retrieves each filing, extracts and normalizes the fields in a two-model AI pass, and routes anything low-confidence to a reviewer.
After
The standard path runs without hand-keying, and reviewers work a short exception queue instead of the whole filing volume.
  1. 01Search registry
  2. 02Retrieve filings
  3. 03Extract and normalize
  4. 04Review exceptions

VC-Backed Mobile Auto Detailing Operator · Automotive Services

Running a Mobile Detailing Business on One Platform

Before
Booking, dispatch, customer communication, and payment collection were split across manual processes and general-purpose tools.
After
A complete, three-surface web platform now connects customers, technicians, and operations staff across paid booking, dispatch, field completion, and receipt delivery.
  1. 01Book and pay
  2. 02Dispatch
  3. 03Complete job
  4. 04Send receipt
See our work

Mid-Market Specialty Commercial Insurance MGA · Insurance

Submission Workbench for Small Commercial Underwriting

Before
Broker emails, loss runs, supplemental applications, and appetite guidelines were spread across systems, so the team could not see whether a submission was complete or in appetite before review.
After
Underwriters receive one consolidated submission with a structured risk summary and explicit missing items before review begins.
  1. 01Collect submission
  2. 02Check completeness
  3. 03Draft risk summary
  4. 04Route for review
See our work

Mandates

Modernize a critical workflow, launch an AI-enabled product, or test whether a proven pattern is worth repeating across your companies or business units.

01

Modernize a critical workflow

For mid-market and enterprise operations and technology leaders.

Example: staff rekeying the same fields out of filings, claims, or emails by hand, while the odd cases that still need a person pile up in a shared queue.

The first deliverable is a scoped build with acceptance criteria you sign off on, and a clear path for the cases a person still needs to see.

02

Launch an AI-enabled product

For product teams, enterprise ventures, and venture-backed companies.

Example: an AI capability inside a product you already sell, such as answers drawn from your own documents or automated intake, that has to hold up in front of real customers.

The first deliverable is a working version running in a real environment, with the data model set, tested against real cases, and ownership settled before you scale it.

03

Evaluate a repeatable operating pattern

For PE operating teams, venture platform teams, and multi-business enterprise leaders.

Example: one workflow that repeats across portfolio companies or business units, such as claims routing or diligence review, proven once before it is copied.

The first deliverable is a clear read on whether the pattern fits and what repeating it would cost, plus one scoped build that tests it in practice.

Bring us the one that sounds like yours.

Start a project

Operating model

Four decisions between the first call and a running system.

Underneath these four decisions is a detailed delivery process. Here is the shape of it.

Review the detailed process

  1. 01

    Frame

    Agree on the workflow, the outcome you want, and who owns what, before we propose anything.

    Discovery and Proposal

  2. 02

    De-risk

    Check that your data can support the system, pick the right models and vendors, and decide how we will prove it works before the full build starts.

    Design and validation planning

  3. 03

    Build

    Build the system, wire it into your existing tools, put checks around it, and test it against the acceptance criteria you signed off on.

    Build and Test

  4. 04

    Operate

    Ship it under the ownership terms we agreed, document it, hand it over, and set up support.

    Deploy and Support

How an engagement starts

Many clients start with a paid discovery or a single de-risking phase, then decide whether to authorize the full build. You get a fixed proposal with scope, milestones, timeline, and price before you commit.

  1. 01Paid discovery or de-risk phase
  2. 02Fixed proposal: scope, milestones, timeline, price
  3. 03You decide whether to authorize the build

Accountability

A named technical lead is assigned at kickoff.

That person stays accountable for the architecture and for the reviews you sit in on, for the full length of the build.

Settled before we start

  • Where the system runs, and who owns the code, data, and infrastructure.
  • What data goes in, and which outside vendors touch it.
  • How the AI is tested on your real cases before it reaches production.
  • Human review where the workflow needs it.
  • Who to call after launch, and what support covers.
Review Security & Trust

Executive Point of View

How we think about AI investment decisions.

Three practical briefs on measuring return, integrating with existing systems, and deciding what to build.

View all insights

AI Strategy · February 4, 2026 · 7 min read

A practical framework for AI ROI

Set the baseline before deployment, separate leading indicators from financial outcomes, and measure value across cost, growth, and risk.

Read the featured brief

AI Strategy · February 10, 2026 · 6 min read

Integrate before you replace

Add a controlled intelligence layer around the systems the business already trusts.

Read brief

AI Strategy · November 11, 2025 · 7 min read

Build, buy, or combine?

Decide against speed, control, data sensitivity, integration depth, and long-term ownership.

Read brief

Buyer FAQ

Questions to resolve before a build.

Clear answers on ownership, validation, procurement, scope, and what happens after launch.

  1. 01

    How do you validate AI before it reaches production?

    We define what success looks like for your workflow, then test the system on your real cases before it ships. Where the work calls for it, we add human review, keep answers tied back to your own documents, and set thresholds so the system asks instead of acts when it is unsure. You test it yourself before it goes live.

  2. 02

    How is an engagement scoped, priced, and timed?

    Scope and price are set during discovery, before any build work is committed. Many clients start with a paid discovery or a single de-risking phase, then decide whether to authorize the full build. Price goes up with more integrations, messier data, and deeper validation, and with more of the running system for us to operate. It comes down when the data is clean, the workflow is well understood, and one person can make the call on scope. Either way, you get a fixed proposal with deliverables, milestones, timeline, and price before you commit to the build.

  3. 03

    Where will the system run, and how is ownership handled?

    The deployment and ownership model is settled during design and contracting. Vectrel can build on infrastructure you manage, work alongside the systems you already own, or run some parts in your systems and some in managed services, with the split written down. Ownership is settled item by item: the source code, the data, the infrastructure it runs on, anything the system produces, and who operates it after launch. Nothing is left to assumption.

  4. 04

    Can Vectrel work with our existing systems and data?

    Yes, when the existing environment can support the use case. Discovery maps workflows, APIs, data sources, security constraints, and integration points before architecture is proposed. Vectrel works with existing systems where that is the right path and scopes any migration explicitly.

  5. 05

    Can you support security, procurement, and NDA review?

    Vectrel can support project-specific discussions about architecture, vendor footprint, data handling, confidentiality, and delivery responsibilities. The exact documents and commitments depend on the engagement and the data involved. Because of the depth of access this work requires, engagements run under NDA and published work is anonymized by default; client references are available on request once an engagement is underway. If regulated, confidential, or sensitive data is in scope, handling boundaries should be agreed before that information is shared through a public channel.

    Review Security & Trust

  6. 06

    Can we start with one team, company, or business unit and expand later?

    A focused advisory or build engagement can stand on its own and also establish the baseline, architecture, and evidence for broader adaptation. If the pattern proves valuable, Vectrel can assess it for additional teams, companies, or business units, revalidating fit, economics, and controls instead of forcing one implementation everywhere.

  7. 07

    What happens after launch?

    Deployment can include configuration, documentation, and team training. It also covers go-live monitoring and the settling-in period right after launch. Ongoing support can cover monitoring in production, incident response, and staying current as models and dependencies change. Who runs it, what support covers, and the service levels are agreed before launch.

Anything else

Something we did not cover? Put it in the intake, or email us.

Start a projecthello@vectrel.ai

Start a project

Bring us the workflow, product, or operating question.

We will identify what to validate first, outline the delivery path, and tell you candidly whether Vectrel is the right fit.

Start a project
See our workReview Security & Trust

Do not include regulated, confidential, or sensitive data in the public intake. If you need an NDA or protected channel first, email hello@vectrel.ai.