HowSo AI Reasoning Engine
HowSo is an explainable AI platform that positions itself as a deterministic reasoning layer for enterprise decision-making, standing apart from black-box neural networks.
Publisher review
HowSo is an explainable AI platform that positions itself as a deterministic reasoning layer for enterprise decision-making, standing apart from black-box neural networks. Founded in 2017 as Diveplane before rebranding in September 2023, the company is based in Raleigh, North Carolina and focuses on what its founders call "Understandable AI®"—transparent, auditable artificial intelligence with full attribution of results back to source data. The HowSo Engine operates on instance-based machine learning, combining a fast spatial query system with information theory to deliver what the company claims are hallucination-free outputs without the opacity typical of large language models.
The platform integrates with enterprise systems including Microsoft Power BI, Fabric, and Copilot, enabling organizations to discover patterns in historical data, predict outcomes, generate recommendations, and critically, explain why those predictions matter. Unlike post-hoc explanation tools like SHAP or LIME that are grafted onto existing models, HowSo's explanations are native to the system—inherent rather than added afterward. The platform serves healthcare (BlueCross BlueShield, Duke Health), financial services (Mastercard), and insurance sectors, targeting use cases from fraud detection and compliance reporting to personalization and maintenance optimization.
Its differentiator is determinism: same inputs always produce the same outputs, with reproducible audit trails. The main tradeoff is philosophical—the company deliberately trades some of the raw predictive power of deep learning for interpretability and control, positioning itself for regulated industries where understanding decisions matters more than squeezing the last percentage point of accuracy.
How it works
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Native Explainability
Built-in transparency for all predictions and recommendations, with automatic attribution tracing decisions back to specific input data points and similarity relationships.
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Fully Deterministic Reasoning
Reproduces identical outputs given the same inputs, eliminating non-deterministic behavior common in neural networks and providing consistent audit trails for compliance.
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Instance-Based Machine Learning
Operates by relating new observations to historical data patterns via spatial proximity, enabling interpretable comparisons and anomaly detection without statistical model fitting.
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Live Data Integration
Connects directly to source systems (data lakes, warehouses, cloud storage) to perform analysis on current data without batch ETL, supporting real-time scenario modeling and what-if analysis.
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Synthetic Data Generation with Watermarking
Creates privacy-preserving synthetic datasets suitable for safe data sharing, with embedded watermarks to track source and detect misuse.
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Causal Discovery
Identifies directional relationships between variables—not just correlations—helping uncover root causes in operational and business data.
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Microsoft Ecosystem Integration
Embeds reasoning capabilities into Power BI, Fabric, and Copilot, allowing business analysts to leverage deterministic AI without specialized data science skills.
Strengths and trade-offs
Strengths
- Native explainability means explanations are not bolted on after training; they are inherent to how the engine reasons, providing genuine transparency rather than approximations.
- Fully deterministic outputs eliminate non-reproducibility issues, critical for regulated industries (healthcare, finance, insurance) where audit trails and consistency are non-negotiable.
- Instance-based approach scales interpretability: the engine can explain individual predictions by showing which historical records drove the decision, making results actionable for non-technical stakeholders.
Trade-offs
- Determinism comes at a cost: the platform explicitly trades raw predictive power for interpretability, potentially underperforming deep learning in domains where accuracy matters more than explainability.
- No public pricing available; enterprise-only sales model requires contacting the vendor, creating friction for evaluation and adoption by smaller organizations or startups exploring the category.
- Limited market visibility despite 2023 rebranding; minimal presence in community discussions (Reddit, Hacker News, public GitHub activity) compared to SHAP, LIME, or mainstream open-source ML tools, suggesting B2B enterprise focus over developer adoption.
Pricing context
HowSo does not publish pricing on its website. The company operates an enterprise sales model requiring direct contact for demos and quotes. Pricing appears to be seat-based or consumption-based (query volume or data size), typical of B2B AI platforms serving healthcare, financial, and insurance sectors.
No freemium tier, trial, or open-source community edition is advertised, though HowSo Engine is available via PyPI as an open-source project for local use. Target customers are Fortune 500 enterprises with compliance and auditability requirements.
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Sources
Reporting on this tool draws on these publicly available sources.
- www.howso.com — Company overview, core offerings (HowSo Engine, Data Insights, Privacy Accelerators), enterprise clients, mission statement
- www.howso.com — HowSo Engine technical capabilities, feature set (live data connection, native explainability, determinism, real-time simulation, causal discovery, anomaly detection)
- wraltechwire.com — Founding year (2017), original name (Diveplane), rebranding date (September 2023), headquarters location (Raleigh, NC), open-source pivot
- www.howso.com — Founders (Dr. Chris Hazard, Michael Resnick, Dr. Michael Capps), founding year, headquarters address, mission statement, vision
- pypi.org — Open-source availability of HowSo Engine, instance-based machine learning architecture