01
Decision intelligence & research
Systems that continuously collect, structure, compare and analyze information to support better decisions.
Investment researchDue diligenceMarket intelligencePortfolio monitoring
What we build
Five kinds of AI-native systems
Each one starts from a real operating problem, not a generic AI initiative.
02
Workflow & operating agents
Agents that take part in recurring business processes rather than just answer questions: monitoring data, catching exceptions, preparing recommendations, updating systems and following up.
Sales operationsAdvertising managementOrder processingBack-office workflows
03
Knowledge & document intelligence
Fragmented documents and communications, made into an active operating layer: retrieve evidence, extract and compare, answer in context, and preserve institutional memory.
Document reviewInternal knowledge systemsCompliance supportProfessional-service workflows
04
Operational AI & optimization
AI connected with inventory, demand, supply, pricing, quality and production constraints, combined with optimization models for day-to-day operating decisions.
Supply chainProduction planningFormulationScheduling & pricing
05
Client-facing AI products
Proprietary expertise, assessment methods and service workflows, turned into scalable digital products with intelligent onboarding, analysis, recommendations, reporting and expert review.
Advisory platformsDiagnostics & assessmentsRecommendation systemsCustomer portals
Not sure where yours fits?
Bring one concrete workflow and we will judge together whether it is a good AI-native candidate.
01—04
How we build
Every workflow is mapped as one complete loop, then run end to end on real data and real tools.
inputjudgmentactionoutputfeedbackaudit
01
Find the right wedge
Start from the workflow: where time goes, which decisions repeat, where people move information between systems by hand. Pick a use case valuable enough to matter, narrow enough to build quickly, and measurable enough to judge honestly.
02
Build a working vertical slice
Build the smallest complete system that runs the whole loop, to get fast feedback and expose the real engineering and operating constraints, instead of starting with a platform replacement.
03
Engineer for real operations
An agent is not a prompt. Data access, tool use, business rules, human review, evaluation and regression tests, observability, failure handling and rollback all have to be designed in.
04
Transfer capability
We document the architecture, code, configuration, tests and operations, and work directly with your technical or data team so they can understand, modify and eventually own routine extensions.
Engagements
From a briefing to a long-term engineering partner
Start where you are. The aim is always for routine iteration to move inside your own team.
0.5–1 day
Executive AI briefing
A practical frame for leadership on where AI could change the economics or operating model of the business: industry patterns, workflow-level opportunities, risks, and what merits a closer look.
1–2 weeks
AI opportunity sprint
For many AI ideas and no clear first move: map the highest-value opportunities, study real workflows and data, prototype selected concepts, and set a near-term roadmap. It answers where to start and what is worth building.
4–8 weeks
Builder enablement
Co-build a production or near-production reference system around one real workflow with your internal team: architecture, core build, evaluations, documentation, controls and operating model. It becomes a repeatable engineering pattern, not a one-off outsourced tool.
3–12 months
AI engineering office
For organizations past their first AI system and building a portfolio: ongoing architecture review, complex upgrade sprints, evaluation and governance design, new use cases, and support for internal AI builders. The goal is not permanent dependency.
Client work
Five industries, two patterns
Anonymized where appropriate. Each one is an operating pattern that transfers to other industries.
02 cases
Operations & execution
AI inside the daily operating loop: spotting issues, recommending, and acting within set permissions.
Global e-commerce & consumer products
Advertising operations
For a global consumer-products company with major Amazon operations: an AI system that diagnoses ad performance, flags anomalies and opportunities, recommends bid, budget, keyword and campaign changes, and executes within defined permissions. The client's own team maintains and extends it.
observe → analyze → recommend → act → evaluate
Also relevant to
Digital commercePerformance marketingRevenue operationsPricing
Agriculture & industrial operations
In progressCommercial, field & production intelligence
With a large agriculture and animal-nutrition group: reading feed orders from messaging channels and linking them to inventory and pickup, using farmers' recurring photos and operating data to monitor customer and credit risk, and re-optimizing feed formulas as ingredient supply, quality and cost shift.
Also relevant to
AgricultureFood productionManufacturingSupply chain
03 cases
Decision & knowledge work
Scattered information and expert judgment, turned into decision support you can reuse and trace.
Investment management
Investment intelligence
For an investment firm: an AI-native platform that turns emails, research and company materials into living project knowledge bases, with standardized screening, company and portfolio monitoring, research tracking, and natural-language questions over everything collected.
Also relevant to
Venture capitalPrivate equityFamily officesCorporate development
Capital markets & professional services
In progressExpert-service platforms
For a professional-services firm: turning proprietary methodology into a scalable digital workflow, with structured onboarding, AI-assisted document analysis, evidence and risk checks, configurable scoring and automated diagnostic reports. Human review stays on consequential calls.
Also relevant to
Financial servicesAdvisoryAccounting & legalCertification
Public sector & program evaluation
Review & assessment
For a municipal employment-services program: an AI-assisted review system that organizes evidence from sites and submissions, spots gaps and inconsistencies, applies set criteria, and drafts standardized preliminary assessments for human reviewers to decide.
Also relevant to
Government programsGrants & procurementAccreditationMulti-site organizations
Built for human accountability
The goal is not maximum autonomy. It is reliable autonomy at the level the business can justify.
AI systems become more useful as they move closer to real decisions and actions, and they demand stronger engineering discipline. The right level of autonomy depends on the workflow, and it should be earned in steps.
Explicit permissionsHuman-in-the-loop reviewEvaluations & regression testsOperational logsAudit trailsClear ownership
- 01Read and diagnose
- 02Make concrete recommendations
- 03Execute with human approval
- 04Automate within clear boundaries
Why Causally
We build AI-native products ourselves.
We bring the rapid product development, workflow decomposition, agent architecture, evaluation and iteration from our own products into enterprise work. AI transformation is not a software purchase. The deeper shift is organizational: teams learn to break work into decision-and-action loops, define what AI should own, and shorten the distance from idea to feedback.
What we aim to leave behind is not one AI tool, but a working system, a reusable engineering pattern, and a team that knows what to build next.
Who we work best with
You do not need a large AI team.
- A real, recurring workflow
- A meaningful business outcome
- Access to representative data or examples
- An internal owner willing to build alongside us
Just a problem worth solving and people who want to learn how the system should work.