Connect the evidence
Bring together the records your team already depends on: logs, PDFs, spreadsheets, systems of record, documents, reports, and messages. We connect them inside your own cloud, datacenter, or approved region.
AI agents that stay inside your environment · US & Europe
Your company has the data. Proqtor turns it into better decisions.
The problem is not a lack of data. It is that decisions still sit across logs, spreadsheets, PDFs, systems of record, documents, messages, and people’s heads. Proqtor connects that evidence inside your own environment, then uses controlled agents to prepare reports, surface risks, and automate the next step only when the record is clear.
We start with one painful workflow: drafting a report, reviewing a document, triaging a request, or routing an approval. We prove it on real work, then earn the next step.
- Report Drafting Operations Draft + approval
- Document Review Legal Recommended action
- Request Triage Support Execute with approval
- Manual steps cut
- 31%
- Actions approved
- 94%
- Typical answer time
- 3.4s
Your data, files, and prompts stay in your systems.
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Less chasing, more doing
Reports, summaries, and checklists start from the evidence already in your systems, not a blank document.
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Risks show up earlier
Missing records, overdue actions, cost drift, quality issues, and compliance gaps are surfaced before the monthly review.
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Automation stays controlled
Agents draft and recommend first. They act only after the rules, approvals, and record are clear.
01 · The problem
Your company has more data than ever. The hard part is making decisions from it.
Teams already have logs, exports, records, reports, contracts, documents, PDFs, and spreadsheets. The problem is that they rarely line up when someone needs an answer. People spend hours chasing context instead of deciding what to do next.
- 01
Work is scattered
Notes, PDFs, spreadsheets, systems of record, and email threads all tell part of the story. No one has the whole picture fast enough.
- 02
Risk arrives late
Compliance gaps, quality issues, cost drift, and missed follow-ups often appear after the cost is already visible.
- 03
Too much lives in people’s heads
Your best people know what matters, but that knowledge is hard to share across teams, sites, vendors, and managers.
Built for teams where a missed detail can affect cost, quality, timelines, compliance, or an audit
- Operations
- Finance
- Legal
- Customer support
- IT
- Compliance
We start where evidence is scattered and decisions are slow. One workflow first, one clear result, then the next step when the record supports it.
02 · The platform
A practical layer for your decisions, built inside your environment.
Prepare the work
Agents draft the things teams lose time on: report summaries, document reviews, request follow-ups, approval checks, missing-document lists, and manager briefings. People review the work before it moves.
Show the source
Every answer points back to the records it used, who approved it, what changed, and what still needs attention. Managers get a plain record they can trust, not a black box.
Automate the next safe step
Once the record is good enough, the agent can take one approved next step: open a follow-up, prepare a report, route a request, or update a record. Bigger actions wait until the evidence is there.
03 · Private by design
Your data stays with you.
Every file, question, document, note, and record stays inside your environment. We fit the deployment to your privacy rules, access policies, and data-residency needs, then run the controls through access you approve.
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Your records stay yours
Logs, documents, reports, contracts, records, questions, and agent runs stay in your environment.
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We work through approved access
We handle setup, monitoring, and support through the access you grant us, never around it.
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One checkpoint for every request
Each request checks who can access what, masks sensitive details when needed, keeps spending limits, and records which model was used.
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The agent only sees what the person can see
An agent can only open the files and records the assigned user is already cleared to access. Nothing more.
- RUNS IN
- Your cloud account
- DATA LEAVING
- None reaches us
- ENCRYPTION KEYS
- Yours, held by you
- GOOD FOR
- Most companies
- RUNS IN
- Your datacenter
- DATA LEAVING
- Can run fully offline
- HARDWARE
- Yours
- GOOD FOR
- Strict on-prem requirements
- RUNS IN
- A US or EU region
- WHERE DATA LIVES
- Stays in that region
- CROSS-BORDER
- No EU data leaves the EU
- GOOD FOR
- GDPR · group reporting
- RUNS IN
- The device itself
- DATA LEAVING
- Works offline
- MODELS
- Made small enough to run on the device
- GOOD FOR
- Field work · offline sites
04 · Proof it works
See the work, the risk, and the next action.
This is not a developer dashboard. It shows managers what the agent prepared, which sources it used, what still needs approval, and what is safe to automate next.
31% Manual steps cut
94% Actions approved
2.1% Open risk rate
3.4s Typical answer time
91% Right source found
78% Adoption · teams
- 09:24 report-drafting Weekly status report $0.12 approved
- 09:21 doc-review Missing signature page $0.19 awaiting
- 09:18 ticket-triage Request R-7184 $0.21 approved
Each agent has one clear job, an owner, a scope, and a check before action
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Report Drafting
Draft + approval- Owner
- Operations
- Scope
- Recurring reporting · Spreadsheets · documents · systems of record
- Test
- time to a reviewed draft
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Document Review
Recommended action- Owner
- Legal
- Scope
- Contract and document review · Document store · PDFs · records
- Test
- issues found before sign-off
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Request Triage
Execute with approval- Owner
- Support
- Scope
- Incoming requests and tickets · Ticketing · email · knowledge base
- Test
- time to first response
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Approval Routing
Draft + approval- Owner
- Finance
- Scope
- Reviews and approvals · Email · spreadsheets · systems of record
- Test
- overdue approvals cleared
05 · Autonomy
Automate only when the results say it’s safe.
Every workflow follows the same step-by-step path, where an agent earns more responsibility only as it proves itself.
- L0
Assistant
Answers and drafts. A human does everything.
- L1
Draft + approval
The agent proposes; a human approves every action.
- L2
Recommended action
The agent recommends the next step to take.
- L3
Execute with approval
The agent acts after a human signs off.
- L4
Execute under policy
The agent acts within set guardrails; humans audit.
- L5
Autonomous
Scheduled and self-running; humans handle exceptions.
The governance horizon
Governed agents aren’t optional: the rules are already here.
- EU
AI Act prohibitions
Banned practices in force
- US
Federal AI guidance
Procurement & testing (OMB)
- EU
AI Act applies
Most obligations in force
WE BUILD FOR THIS - US
State AI laws
Colorado, California, Texas
- EU
High-risk systems
Product-integrated AI obligations
06 · How we work
Understand the problem. Scope a pilot. Decide what’s next.
- STEP 01 1–2 weeks START HERE
Understand the problem
We start by understanding the problem, not pitching a solution. Our engineers sit with the people who run the work, learn where decisions slow down, and map the records your team already trusts. We come to learn the work in order to build it, not to write a report.
OUTPUT A clear, shared definition of the problem, the evidence needed, and where an agent would actually help.
- STEP 02 30–45 days
Scope a pilot
Together we pick one painful workflow first and agree what the pilot will prove: what success looks like, which sources count, who approves action, and where the agent must stop. Then we build and run it inside your environment. The agent watches and suggests before it is allowed to act, then takes one approved low-risk live step.
OUTPUT One workflow proven on real work, on a scope you signed off.
- STEP 03 after the pilot
Decide what’s next
With the pilot’s results in hand, we decide together how to continue: expand to the next workflow, adjust the approach, or stop. The evidence makes the call, not a contract. If it is working, we plan the next workflows with you and build them out, one at a time.
OUTPUT A decision backed by real results, on your terms.
Engagements
Each engagement solves one specific problem.
Hands-on service now, a product later. We develop and ship a solution built around your company, your systems, your privacy rules, and the approvals your team needs. Each engagement is a step on the same path: land one workflow, prove it works, and build the clear record every future workflow runs on.
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A first pilot
A workflow depends on scattered evidence and too much manual chasing.
In 30–45 days: one private workflow where the agent prepares the work, shows the sources, and takes one approved low-risk live step. You get a clear report of what improved, what still failed, and what is safe to automate next.
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Decision support
Managers need answers, but the story is split across systems, files, and people.
A single place to ask what happened, see the source records, compare options, and decide the next action with confidence.
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A setup your security team will approve
Customer, financial, and internal data cannot be copied into outside AI tools.
Agents run inside your approved environment, follow your access rules, and keep a record of every source, approval, and action.
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Scale it up, safely
One workflow works; expanding without losing control is the hard part.
We add new agents and connections inside your team, giving each workflow more responsibility only as the evidence supports it.
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Keep it useful
Processes change, reports drift, and no one owns keeping the automation current.
Ongoing checks, replay tests, updates to sources and rules, and a quarterly review of what to automate next.
07 · The arc
Where this is going.
Proqtor is early, and focused on purpose. Here is the plan, in plain sight.
- NOW
Land
One workflow, deployed inside your own environment and proven on real cases before it touches production. We embed in your team and build it end to end.
- NEXT
Expand
One workflow at a time, each its own product. We go where an agent can affect cost, quality, timelines, compliance, customer commitments, or audit readiness, and only when the last one has proven it works. For example: report drafting, document review, request triage, approval routing, records checks, and vendor reviews.
- THE PLATFORM
Productize
Under every workflow is the same clear reporting: cost per completed task, approvals, a record of every step you can replay, and what is safe to automate next. That reporting is the part we are building into a product. Hands-on service first, because that is how you learn a workflow honestly. A product second, because that is what the reporting is for.
This is the plan, not a finished product. Today, the work is hands-on, one workflow at a time.
ALBERT GARCÍA HERNÁNDEZ · FOUNDER
08 · About
We deploy our engineers, not just our software.
Proqtor is built and run by its founder, Albert García Hernández, and the team works by one rule: the people who build your system work inside it. We are forward-deployed.
Forward-deployed is the model Palantir made its name on. The engineers who build the software work alongside your team, on your real cases, and stay until it runs in production, instead of selling a license and walking away. We hold to that standard at founder scale: whoever plans your deployment also builds the agents, sets up the reporting, and signs off on the security setup. No junior bench, no plan handed to someone who has never seen your work.
And we come to where the work is. We serve teams across the US, Europe, and beyond, deployed in your cloud, your datacenter, or on-site, always inside the boundary that holds your data, and built to fit the policies and regulations you already operate under.
- Private deployment
- AI that works from your own documents
- Document understanding
- Reliable automation in production
- Built inside your team
Private AI for your operations · built inside your team · international
09 · Blog
Notes on running AI agents in production.
Why 40% of agent projects get cancelled, and the four controls that save them
Gartner expects most agentic AI projects to be scrapped by 2027. The failures are operational, not model quality, and four controls keep a project alive.
Read
The EU AI Act and US state laws for autonomous agents: what is enforceable now
A dated, sourced map of AI agent rules in mid-2026: which EU AI Act and US state obligations apply today, which are delayed, and what to build for now.
Read
What actually kills agent deployments (and how to not be a statistic)
Gartner expects 40% of agentic AI projects cancelled by 2027. The failures are operational, not model quality. Six documented patterns, the control for each, and a pre-mortem.
Read
When your AI vendor retires the model you rely on, your work can break overnight
When a vendor retires or quietly changes the AI model your work depends on, the instructions you tuned, the tests you trust, and your costs can break overnight, with no change on your side. Here is how to stay ahead of it.
Read
Start here
Tell us where the work gets stuck.
We start with one painful workflow: a 30–45 day pilot inside your own environment that ends with clearer decisions, less manual chasing, and a record of what is safe to automate next.