info@rocketvector.io

Causal AI, decision support, and deployment

Move from prediction to intervention.

Rocket Vector helps teams answer a practical question: if we change this, what happens next? We build causal models, decision tools, and deployment paths for organizations that need more than descriptive analytics.

  • Explain what is driving an outcome instead of stopping at correlation.
  • Turn the work into a pilot, API workflow, trial software path, or custom application.
  • Support teams that need traceable reasoning, practical delivery, and a clear next step.
See Engagement Options Email Rocket Vector
Best Fit Complex decisions with multiple interacting drivers, meaningful tradeoffs, and limited tolerance for ambiguity.
Delivery Scoped pilots, technical evaluations, deployable APIs, trial software, and custom decision tools.
Proof Public demos, technical docs, trial builds, and procurement identifiers are available now.
Abstract visualization representing Rocket Vector's causal modeling platform

Understand drivers, not just correlations

Compare interventions before rollout

Start with a pilot, evaluation, or application

Start with a narrow scope, then expand based on results

What We Do

Rocket Vector helps organizations move from “what happened?” to “what should we change?” and deliver the answer in a form teams can actually use.

From data to action

A short overview of the Rocket Vector approach to causal reasoning, intervention planning, and deployable decision support.

Rocket Vector combines causal discovery, Bayesian networks, intervention analysis, counterfactual reasoning, and delivery engineering so teams can turn messy data into a decision-ready workflow.

The work is strongest when leadership needs more than a forecast. They need to know what changed, what can be changed next, and how those answers can be delivered to analysts, operators, clinicians, or product teams in a usable form.

  • Work with analytics, research, operations, clinical, and product teams making consequential calls.
  • Deliver through cloud, private-environment, API, SDK, trial software, and custom-application paths.
  • Focus on traceable reasoning for regulated, governed, or otherwise high-consequence environments.

Most engagements start with one real workflow, one real decision owner, and one concrete next step: a pilot, a technical evaluation, a trial build, or a custom implementation path.

Proof and Readiness

If you are evaluating whether Rocket Vector is real, start with the work you can inspect today.

Public demonstrations

Explore live examples in healthcare, policy, and safety data rather than relying on abstract claims alone.

Inspectable technical artifacts

Rocket Vector publishes documentation for the cloud API and supporting Python libraries used in real technical evaluations and implementations.

Delivery beyond a slide deck

Rocket Vector can deliver causal work as a pilot artifact, API workflow, SDK-backed handoff, private-environment deployment, or custom application depending on the team and constraints.

Procurement-ready company details

For teams that need vendor setup or public-sector procurement information, Rocket Vector maintains the identifiers below.

  • UEI: QMPBWBAHJK66
  • CAGE/NCAGE: 9T1B5
  • SDVOSB: Certification pending

What Problems Are a Fit

Rocket Vector is strongest when the decision matters, the data is imperfect, and somebody has to act on the answer.

Strong-fit situations

  • Intervention planning. You need to compare scenarios, policies, or treatment options before changing a workflow or committing resources.
  • High-consequence environments. The team needs reasoning it can inspect, explain, and defend rather than a black-box score alone.
  • Operational delivery. A report by itself is not enough. The output needs to live inside an API, internal tool, or custom application.
  • Messy observational data. The decision depends on interacting drivers and tradeoffs that standard dashboarding or correlation analysis does not resolve.

Representative domains

  • Healthcare and clinical decision support. Explore diagnoses, treatment pathways, risk factors, and counterfactual questions when decisions have real consequences.
  • Program evaluation and policy analysis. Model drivers, interventions, and tradeoffs when teams need to compare options before rollout.
  • Operational risk and resource allocation. Identify which levers meaningfully change outcomes instead of optimizing around unstable correlations.
  • Decision-support interfaces. Wrap causal models inside analyst-facing or customer-facing applications so insights can be used in day-to-day workflows.

Usually not the right fit

  • Teams looking only for a commodity dashboard, generic BI work, or a one-off slide deck.
  • Projects where there is no decision owner, no intervention to evaluate, or no path from analysis to action.
  • Situations where a simple forecast is sufficient and causal reasoning would add complexity without changing the decision.
Demonstration of a conversational interface paired with Rocket Vector's causal analysis

One example integration

Generative AI + Causal AI. Rocket Vector has also shown an integration with Autonosis that combines conversational interaction with causal inference for differential diagnosis support.

Engagement Options

Engagements usually start with one clear buying motion, not a vague transformation pitch.

Pilot Engagement

Start with one decision, one workflow, and one concrete output such as an intervention analysis, causal model, or prototype workflow.

Technical Evaluation

Test API workflows, SDK-backed usage, trial software, data constraints, and deployment assumptions before committing to a broader implementation path.

Custom Decision Application

Build analyst tools, decision-support interfaces, or customer-facing applications that wrap the model inside a workflow your team can actually adopt.

Deployment and Handoff

Support production deployment, integration planning, documentation, and internal handoff when you need the work to continue beyond the pilot phase.

Flagship Product: LeadScope

Rocket Vector supports multiple product and delivery paths. Among them, LeadScope is the flagship workflow for lead optimization and molecular engineering.

What LeadScope Does

LeadScope helps discovery teams answer a practical question: what analog should we make next? It is built for lead optimization workflows where score-only ranking still leaves the decision unclear.

How It Fits the Stack

LeadScope sits on top of existing molecular generation, docking, ADMET, and screening workflows. The goal is not to replace the stack, but to turn its outputs into intervention-oriented design guidance.

How It Starts

The strongest commercial motion is narrow: one series, one target, one memo, one pilot. That keeps the first engagement concrete while showing how Rocket Vector's broader causal approach applies to drug discovery.

Why Teams Care

  • Reduce wasted synthesis cycles and shorten design-test-decide loops.
  • See tradeoffs across potency, ADMET, developability, and synthetic feasibility earlier.
  • Support rescue-versus-stop decisions before more chemistry spend is committed.

What a First Pilot Produces

  • A decision-ready view of which analog directions appear worth pursuing next.
  • Explicit tradeoffs instead of a longer unexplained ranking list.
  • A clearer recommendation on whether to pursue, modify, rescue, or deprioritize a series.

What Happens After You Reach Out

Most conversations move quickly toward a fit call, a recommended delivery path, and a narrow first deliverable.

Fit and Scoping

Define the operating question, identify the users, review the data, and surface deployment, privacy, or procurement constraints before building anything.

Build the First Useful Asset

Create the first practical deliverable, whether that is a causal model, intervention analysis, technical evaluation, API workflow, or prototype application.

Expand Only If It Earns It

From there, decide whether the next step is production deployment, a broader implementation, a custom application, or a deeper technical engagement.

What the First Engagement Should Produce

  • A framed decision or workflow with clear users and success criteria.
  • A review of the available data and the relevant governance or privacy constraints.
  • A first deliverable that can actually be used: analysis, prototype, API workflow, or application concept.
  • A recommended next path: stop, continue as a pilot, move into deployment, or hand off internally.

Commercial and Procurement Readiness

Rocket Vector supports teams that need formal vendor setup, procurement identifiers, or public-sector-ready onboarding details before a project can move.

  • UEI: QMPBWBAHJK66
  • CAGE/NCAGE: 9T1B5
  • SDVOSB: Certification pending

Technical Access and Delivery Paths

Rocket Vector is not limited to slideware. Technical teams can evaluate APIs, SDK-backed workflows, and deployment patterns during pilots and implementation engagements.

Common delivery patterns

Rocket Vector supports deployable causal modeling workflows for teams that want to embed models into internal tools, customer-facing products, or controlled technical environments.

  • Cloud API workflows. Learn and deploy causal models in managed environments.
  • Python library workflows. Use the py-bbn, py-scm, and pyspark-bbn libraries where teams need direct technical control.
  • Private or controlled environments. Evaluate fit when security, privacy, or governance constraints matter.
  • Custom interfaces. Wrap model logic inside internal tools, analyst workflows, or customer-facing products.

Documentation

Need technical access or evaluation support? Email Rocket Vector and include your technical environment, data constraints, and intended use case.

Illustrative API workflow

Upload a dataset and deployment specification.

curl -X 'POST' \
  'https://api.rocketvector.io/v2/trigger' \
  -H "api-key: [YOUR_API_KEY]" \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'param_file=@deployment-specs.json;type=application/json' \
  -F 'data_file=@data.csv;type=text/csv'

Then issue inference calls against the deployed model.

curl -X 'POST' \
  'https://api.rocketvector.io/v2/inference' \
  -H "api-key: [YOUR_API_KEY]" \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{"deployment_id": "[YOUR_DEPLOYMENT_ID]", "evidence": {"sneezing": "true", "coughing": "false"}}'

Current model support includes discrete and Gaussian Bayesian Belief Network workflows.

Trials

For teams that want hands-on evaluation first, Rocket Vector offers verified, limited-time trial builds for selected software.

Request a Trial Build

The Rocket Vector build portal lets evaluators request available software directly. Trial builds are available for seven days after verification.

Choose the software, select your platform, agree to the license, and submit the request through the secure build page.

Open Trial Portal Read Trial License

Currently Available

  • Py-BBN. Limited-time evaluation build of Rocket Vector's Python Bayesian belief network library.
  • VizApp. Limited-time evaluation build of Rocket Vector's VizApp software.
  • Platform options. Trial requests currently support Mac, Windows, and Linux selections in the portal.

The trial path is for software evaluation. If you need a pilot, API access, or a custom build instead, the contact path on this page is still the right starting point.

Frequently Asked Questions

Short answers to the questions teams often ask before they reach out.

What does Rocket Vector offer?

Rocket Vector sells a mix of scoped services and software paths: pilot engagements, technical evaluations, APIs, trial software, and custom decision applications. The right entry point depends on whether you need answers, software evaluation, or deployment.

How do I know whether this is a fit?

It is a fit when the decision matters, the drivers interact in ways ordinary dashboards do not explain, and someone needs to act on the result. If all you need is descriptive BI or a generic forecast, this is probably not the right tool.

What does a first engagement usually look like?

Usually one important decision, one narrow workflow, and one useful first deliverable. The goal is to establish fit quickly before expanding into a broader implementation.

What is LeadScope?

LeadScope is Rocket Vector's flagship product for lead optimization and molecular engineering. It helps drug discovery teams decide what analog direction to pursue next when score-only rankings do not make the intervention clear.

Do you only work in healthcare?

No. Healthcare is one strong domain, but Rocket Vector also supports public-sector, policy, operational risk, and other data-rich environments where teams need intervention reasoning and delivery options.

Does LeadScope replace our current discovery stack?

No. LeadScope is meant to sit on top of existing generation, docking, ADMET, and screening workflows. The point is to turn those outputs into clearer design guidance, not force a rip-and-replace of the tools your team already uses.

Can we evaluate software directly before a bigger engagement?

Yes. The trial portal lets you request verified, seven-day evaluation builds for available software, including Py-BBN and VizApp. That path is useful when your first question is about product fit rather than a broader services engagement.

Do trials replace a pilot?

No. Trials are for software evaluation. A pilot is the better path when you need help framing the decision, working through the data, or proving value in a live workflow.

Can you work with governed or private data?

Yes. Rocket Vector can work across cloud, private-environment, and SDK-backed technical paths. The right setup depends on the data, governance, security, and deployment constraints surfaced during scoping.

What does a LeadScope pilot usually produce?

A good LeadScope pilot should end with a decision-ready view of which analog directions appear worth pursuing, what tradeoffs matter across potency and developability, and whether a series looks worth advancing, modifying, rescuing, or stopping.

What should we send in the first email?

Send the decision or workflow you want to improve, the data you have today, who needs the output, and any privacy, security, or procurement constraints. A rough outline is enough if the problem is real.

Is LeadScope the whole company?

No. Rocket Vector remains focused on causal modeling, intervention reasoning, and deployable decision support more broadly. LeadScope is the flagship product for drug discovery, not the limit of the company's work.

Start With a Real Use Case

The fastest way to evaluate fit is a short email with enough context to scope the right next step.

Start with email

Email info@rocketvector.io to start a scoping conversation. This works better than a generic form when the first discussion may include privacy constraints, procurement questions, technical context, or uncertainty about the right entry point.

Use email if you want to discuss a pilot, a technical evaluation, trial software, or a custom engagement. If all you have is a rough problem statement, that is enough to start.

A useful first message usually includes:

  • Your organization, team, or program.
  • The decision, workflow, or outcome you want to improve.
  • The data you have today and any security or privacy constraints.
  • The delivery path you are considering: analysis, API, application, or advisory support.
Discuss a Pilot Discuss Technical Access Connect on LinkedIn

What Happens Next

  • The initial note is reviewed against the decision, data, and delivery path you described.
  • If the fit is strong, the next step is usually a scoped pilot, technical evaluation, or custom engagement discussion.
  • If the best next step is a demo, documentation review, or a narrower scoping conversation, that can happen first.
  • Good first-fit conversations usually involve a real workflow, a real decision owner, and a reason the output needs to be used beyond a report.

If you are not sure whether Rocket Vector is the right fit, send the use case anyway. A narrow scoping conversation is usually the right first step.