DECISION INTELLIGENCE
Outcomes
Better decisions, faster.
Every organization is drowning in signals. The problem isn't access to data. It's the ability to gain insight into data to make decisions at speed, with confidence. We build the intelligence layer that closes the gap.
Before
A rules-based alert system generated large volumes of low-priority, redundant alerts. No dynamic risk context. No severity scoring. Analysts reviewed everything manually across ~28,000 trades per week.
FIS · Financial Services
→
After
Cortex LLM scores each alert with a multivariable risk score weighing violation threshold, sentiment, confidence, and risk categories. Genuine threats route instantly to analysts. Noise stays filtered.
~98.5% reduction in manual compliance review
Before
Analysts manually aggregated macro news across dozens of outlets to brief executive leadership. Slow, inconsistent, and resource-intensive. Quant strategy decisions made on incomplete, delayed signal.
Capital Group · $2.6T AUM · Financial Services
→
After
LLM-based automation produces executive news summaries and sentiment analyses on demand across hundreds of sources. Leadership makes data-driven decisions faster with consistent, scored signal.
Real-time quant decision support. Manual research eliminated.
Before
Clinical teams ran a heavily manual screening process to identify trial participants. Inconsistent criteria application, slow turnaround, and dependency on senior clinical staff created costly bottlenecks.
Mitsui USA · Healthcare / Life Sciences
→
After
LLM-powered pre-screening flags eligible patients for trial feasibility in real time. NLP processes unstructured patient data automatically. Clinical staff focus on confirmed candidates only.
2x faster patient pre-screening · 4x faster build vs. industry standard
Our approach
A decision intelligence layer built on your data, your rules, and your risk tolerance. We provide insights into data for fast and accurate decision making.

Built and operated on Snowflake, Cortex AI, and IBM watsonx.

© IBM Corporation 2026. All rights reserved.
Systems
Accuracy with speed.
Every automation follows a specific architecture. Data flows in, AI processes it in real-time, and the right output reaches the right person, agent, or system. Each implementation is tailored to the client's data and workflow.
Decision Intelligence Architectures →
Not a Reporting Tool
AI That Decides What Matters
We don't build dashboards that require humans to find the insight. We build systems that score, rank, and surface what requires attention — before anyone asks.
Not a Black Box
Explainable Scoring You Can Trust
Every decision signal carries a confidence score and a reason. Analysts see why an alert fired, why a recommendation ranked first, and what data drove the outcome.
Not Generic Models
Trained on Your Data
We don't deploy off-the-shelf models. Every scoring system is trained on your data, tuned to your business rules, and validated against your historical outcomes before it goes live.
Not Static
Learns With Every Decision
Human feedback loops refine the model over time. Each analyst action — accept, reject, escalate — sharpens accuracy. The system compounds value the longer it runs.
“Why do we love Hakkoda? Because Hakkoda delivers. Not only is Hakkoda able to deliver, they're able to truly partner with Jefferies as a firm and be creative about solutions. So we really feel like we're in it together.”
Steven Kim, CIO of Corporate Technology · Jefferies Group
© IBM Corporation 2026. All rights reserved.
Success
Real Results Across Industries.
Not presentations. Not projections. These are production decision intelligence systems operating live for global enterprises at scale.
98.5%
Reduction in manual compliance review volume
FIS
2x
Faster clinical trial patient pre-screening
Mitsui USA
40%
Efficiency gains in legal document review
Jefferies
Client results
More results, more industries.
Production-grade decision intelligence deployed across financial services, capital markets, healthcare, retail, and supply chain.
Client
Automation type
Result
FIS
Financial Services
AI trade alert classification using Snowflake Cortex and LLM-based risk scoring. Replaced a rules-based system generating high volumes of redundant alerts.
~98.5% reduction in manual review volume
FIS
Financial Services
AI trade alert classification using Snowflake Cortex and LLM-based risk scoring. Replaced a rules-based system generating high volumes of redundant, low-priority alerts.
~98.5% reduction in manual compliance review volume
Jefferies
Capital Markets
Intelligent Agreements — AI-powered legal document review and automated approvals. Snowflake-native MDM unifying 30+ siloed financial instrument data sources.
40% efficiency · 65% cost reduction in document review operations
Capital Group
Financial Services · $2.6T AUM
Automated web scraping and LLM-based sentiment analysis for quant trading strategy. Executive summaries generated on demand across hundreds of news sources.
Structural proof
Manual research eliminated. Quant decisions data-driven in real time.
Mitsui USA
Healthcare / Life Sciences
LLM-powered clinical trial pre-screening and patient feasibility scoring. Natural language processing on unstructured patient data.
2x faster · 4x build speed vs. industry patient pre-screening to confirmed candidates
Medtronic
Healthcare · $33.5B revenue
AI supplier intelligence app built on Cortex Analyst. Three semantic models connected. Conversational interface surfaces procurement KPIs and negotiation recommendations instantly.
Structural proof
Built and in production in 8 weeks. Medtronic approved enterprise-wide rollout immediately after deployment.
Topgolf Callaway Brands
Retail / Lifestyle
ML propensity scoring and next best action models in Snowpark. Customer Data Platform enabling cross-brand customer intelligence across Topgolf, Callaway Golf, and TravisMathew.
16 weeks to live Two brands migrated, propensity models and campaigns in production
Texas Capital Bank
Banking
End-to-end data governance enabling confident regulatory decisions. Data Vault consolidation providing a single source of truth across all reporting.
72% faster · 5 months to full end-to-end data governance
Take a Look
Financial Services
AI Trade Alert Classification
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Vertical TBD
Decision Intelligence Video 2
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The Data Innovation Journey
Stage 01
Chaos
→
Stage 02
Order
→
Stage 03
Insight
→
Stage 04
Innovation
Don't Get Left Behind.
© IBM Corporation 2026. All rights reserved.
Stage 01
Chaos
Data is scattered across siloed, legacy systems. No unified visibility. No reliable insight. No foundation for intelligence, automation, or modern AI innovation.
Stage 02
Order
A modern data stack is in place. Data is centralized, pipelines are clean, and the organization can finally trust what it's looking at.
Stage 03
Insight
Data operates as a trusted asset across the organization. A single source of truth drives reliable intelligence, faster decisions, and measurable business impact at every level.
Stage 04
Innovation
Organizations at this stage have fully adopted and are operating on a modern data stack, deploying AI for automation and intelligence, launching data products, and opening revenue streams that weren't possible before. With an AI-first infrastructure and mindset in place, they move fast on emerging technology that slower adopters can't, leaving the competition behind.