We create intelligence systems that learn, adapt, and evolve.
Applied AI research for problems that have no ready-made solution.
We don't apply generic models. We design new artificial intelligence architectures that combine advanced research, adaptive systems, and production engineering.
Our Principle
Most real-world problems don't need another model.
They need a new intelligence architecture.
Real data is imperfect.
Real environments change.
Real decisions have a cost.
That's why we don't train models — we create complete intelligence systems.
An applied AI research lab
DataSpoc is not a data consultancy. It's not a software house. It doesn't implement ready-made frameworks.
We are a research and engineering lab in applied artificial intelligence.
We work at the frontier between academic research and real production problems. When your organization faces a challenge where generic AI doesn't solve it, we design the architecture that does.
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Our expertise lies at the intersection of:
- Continuous and adaptive learning
- Bio-inspired models and associative memory
- Temporal intelligence and forecasting systems
- Reinforcement learning on architectures
- AI that evolves in production with governance
How We Think About AI
Intelligence is systemic
It emerges from the interaction between memory, perception, prediction, and decision—not from a single isolated algorithm.
We build systems where AI components communicate, learn from each other, and evolve like an organism.
Learning must be continuous
Static models become obsolete in the real world.
We create AI that learns from its own use—based on operational data, real feedback, and observed results. The system gets smarter the more it is used.
Architecture matters more than the algorithm
The way AI components connect, evolve, and are governed determines whether a system is merely competent or truly intelligent.
We design these architectures from scratch.
Our Proprietary AI Architectures
SpocOne
Behavioral Signatures and Bio-Inspired Memory
Technical Foundation
Architectures based on behavioral signature representation and hierarchical associative memory. Inspired by biological pattern recognition systems.
What It Solves
Discovery of complex patterns in environments where labels are scarce, data is noisy, and behaviors are non-linear.
How It Works
- ›Creates compact representations of multi-dimensional behavior
- ›Builds associative memory that connects similar patterns
- ›Detects anomalies and changes without explicit supervision
- ›Explains discoveries through interpretable signatures
Applications by Sector
When to Use
- ›You have a lot of data but few labels
- ›Important patterns are rare or emerging
- ›Explicit rules do not capture the complexity
- ›You need to discover what you don't know exists
Problems We Solve Across Sectors
Complex environments with many variables and non-linear interactions
Imperfect data: noisy, incomplete, asymmetric
Dynamic problems that change over time
Critical decisions with a high cost of error
Scenarios without a 'perfect dataset' or ready-made benchmark
When generic AI doesn't solve it, architecture makes the difference.
Use Cases by Sector
Detection & Prevention
- ›Adaptive fraud without predefined rules
- ›Money laundering with emerging patterns
- ›Multi-dimensional credit risk
- ›Anomalous transaction behaviors
Forecasting & Anticipation
- ›Default prediction 60-90 days in advance
- ›Real-time portfolio deterioration
- ›Market and liquidity risk
- ›Changes in customer behavior
Optimization & Decision
- ›Dynamic pricing of financial products
- ›Portfolio optimization with complex constraints
- ›Risk-return based capital allocation
- ›Collection and recovery strategies
Research with Purpose
Research without impact is not enough.
Every architecture created at DataSpoc is designed from the start to:
It is born for the real world.
AI as a Service
You don't hire isolated projects. You consume proprietary AI architectures operated as a service.
All models, systems, and technologies are developed, operated, and evolved by DataSpoc.
Custom Intelligence
Custom Intelligence as a Service
What it includes:
- ›Exclusive AI architecture design for your problem
- ›Use of SpocOne, ForecastGPT, and/or Cowpilot
- ›Production-ready APIs, pipelines, and services
- ›Continuous evolution based on real use
- ›Full governance, explainability, and auditing
Ideal for:
Complex problems without a ready-made solution. Organizations that need AI that evolves with their business.
→ You consume intelligence, not infrastructure.
Forecasting & Decision
Forecasting & Decision Intelligence as a Service
What it includes:
- ›Multi-variate and explainable forecasts
- ›Detection of deviations, anomalies, and regime changes
- ›Prioritization and decision support
- ›Continuous learning with operational data
Ideal for:
Operations that depend on anticipating behaviors and detecting changes quickly.
→ Forecasting as a critical infrastructure.
Adaptive Learning Platform
Continuous Learning Platform as a Service
What it includes:
- ›Automatic continuous learning
- ›Creation of features, rules, and strategies
- ›Direct optimization of business metrics
- ›Performance, drift, and cost monitoring
Ideal for:
Systems where competitive advantage comes from continuous improvement.
→ Your AI gets smarter the more it's used.
What's included in all offers:
The technology belongs to DataSpoc.
The value is generated for your business.
Who Works with DataSpoc
Operate complex systems where decisions matter
Deal with imperfect real-world data
Need AI that keeps pace with business dynamics
Do not find a ready-made solution in generic frameworks
Especially where off-the-shelf AI does not deliver a competitive advantage.
Our typical clients:
What they have in common: hard problems, real data, costly decisions.
Why DataSpoc
We design, not apply
While many apply AI, we design new architectures. Applied research with a purpose.
Systems that learn
While many train models, we create systems that continue to learn in production.
Cumulative intelligence
While many deliver point solutions, we build intelligence that accumulates over time.
Operation, not just delivery
We don't deliver models for you to operate. We operate AI systems for you to consume.
Native governance
Explainability, auditing, and control are not add-ons. They are part of the architecture from the start.
Continuous evolution
Your AI doesn't become obsolete. It evolves continuously, under governance, based on real results.
From Discovery to Continuous Operation
Discovery and Architecture
2-4 weeks
- ›Deep problem mapping
- ›Analysis of data, systems, and operational context
- ›Custom AI architecture design
- ›Definition of technical and business success metrics
- ›Solution proposal with roadmap
Deliverable: Architecture document + implementation plan
Development and Validation
4-8 weeks
- ›Construction of the AI architecture
- ›Backtesting with historical data
- ›Validation of performance, explainability, and bias
- ›Iterative adjustments based on feedback
- ›Preparation for production
Deliverable: Functional system + validation report
Production and Monitoring
Initial 8-12 weeks, then continuous
- ›Deployment in production environment
- ›Shadow mode: parallel operation with existing systems
- ›Comparison of decisions and results
- ›Gradual go-live with intensive monitoring
- ›Adjustments based on real behavior
Deliverable: System in production + operational dashboards
Continuous Evolution
Ongoing
- ›Continuous learning with operational data
- ›Evolution of the architecture under governance
- ›Drift and performance monitoring
- ›Monthly impact reports
- ›New features and capabilities
Deliverable: Intelligence that improves continuously
Security, Compliance, and Governance
Our Methodological Approach
Deep understanding: We don't start with models. We start by understanding the problem, data, operational context, and constraints.
Architecture design: We design the intelligence architecture that solves the specific problem—we don't adapt generic solutions.
Rigorous validation: We test exhaustively with historical data, edge cases, and adversarial scenarios before production.
Incremental deploy: We start in shadow mode, compare with existing systems, and go into production in a controlled manner.
Governed evolution: The system learns continuously, but all evolution passes through validation and approval gates.
Research that Generates Value
DataSpoc exists at the intersection of academic research and production problems.
Published in:
Collaborations with:
Research institutes, top universities
Developed:
Patents in adaptive learning systems
But for us, research only makes sense if it generates real impact.
We don't research for the paper. We research to solve problems that the industry doesn't yet know how to solve.
When we publish, it's because we've created something that advances the state of the art. When we patent, it's because we've invented something that works in production.
Who Is Behind It
Researchers and engineers with a background in:
- Advanced Machine Learning and Deep Learning
- Complex and dynamic systems
- Optimization and decision theory
- Production software engineering
Education:
PhDs and master's degrees in AI, Computer Science, Applied Mathematics
Experience:
Google Research, AI labs, unicorn fintechs, investment banks, cutting-edge technology companies
We are not a consultancy. We are an applied research lab that solves problems through advanced AI.
Your Next Step
If your problem doesn't fit into a ready-made model, maybe it needs a new AI architecture.
Generating value for
Diverse sectors, complex problems, one approach: AI architectures that work in production.