Lighter Physician Workload
Generative AI
Generative AI turns your data into original text, images, code, video and audio. We turn generative AI into a governed, reliable, revenue-grade capability. Fast to ship, safe to scale, easy to audit.
Mu Sigma turns GenAI into a governed, revenue-grade capability: we codify domain context with ontologies, knowledge graphs, and question networks to anchor outputs, orchestrate multi-agent workflows with muTalos to draft, review, and verify, capture reusable prompts, macros, and facts in muUniverse to compound learning, enforce LLMOps with a model garden that tracks cost, risk, and drift, and operate it all through CSaaS to ship fast, learn weekly, and scale what works.
We plug into Azure, AWS, GCP, or on-prem, secure data, police PII, red-team for safety, and log full lineage from prompt to decision so audits land clean. We frame the right problems with the Enquiry Engine, reuse EoC assets to accelerate builds, and monitor quality with evaluation harnesses tied to business KPIs. Result: launch quickly, govern confidently, audit easily, and prove measurable impact across real workflows.
Our Work in Action
Ulcerative Colitis Severity Assessment Powered by Vision Models
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90%
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Higher
Chance of Drug Approvals
GenAI Solution Lifts Customer Sentiment for Global Tech Brand
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6%
Lift in Brand Sentiment
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15%
Increase in Repeat Purchases
Boosting Marketing ROI with GenAI
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15-20%
Increase in Marketing Productivity
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18%+
Enhanced Marketing ROI
Why Mu Sigma for GenAI
Semantic Guardrails
We wire in ontologies, knowledge graphs, and question networks to ground prompts, retrieval, and outputs in your business logic. We tag PII, codify policies, and version vocabularies so content stays compliant and on brand. We test for bias and drift, score retrieval quality, and keep model cards and lineage current so audits land clean.
Agentic Operations
We choreograph multi-step work with muTalos. Planner, drafter, reviewer, and QC agents coordinate with humans in the loop to draft, verify, and publish. We add fallbacks, retry logic, and SLAs, connect to data and apps, and capture every run for reproducibility, cost control, and continuous improvement.
Decision OS
We unify data, models, processes, and metrics into a living system that learns. muUniverse stores reusable prompts, macros, facts, and evaluations. The Enquiry Engine frames the right problems and ties outcomes to KPIs, while dashboards track accuracy, adoption, cycle time, and value per workflow.
Bring Your Stack
We integrate with Azure, AWS, GCP, or on-prem and fit to your tools rather than forcing new ones. Use open or commercial models through a common interface. We secure the path end to end, deploy by API or batch or agents, and monitor cost, latency, and quality so you scale without lock-in.
How Mu Sigma’s Delivers Value with GenAI Solutions
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Start with the right base, not the biggest. We select open or commercial models, extend tokenizers for domain terms, and continue pretraining on curated, de-duplicated, policy-clean corpora. We add safety filters, multilingual support if required, and run red-team tests. Deliverables include the checkpoint, model card, data sheet, safety report, and a cost-latency profile so you know what it takes to run at scale.
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Fine-tuning with labeled, domain data to match tone, structure and accuracy. RLHF (reinforcement learning with human feedback) to align outputs with human preferences. Guardrails and safety tests to enforce policy.
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Start with the right base, not the biggest. We select open or commercial models, extend tokenizers for domain terms, and continue pretraining on curated, de-duplicated, policy-clean corpora. We add safety filters, multilingual support if required, and run red-team tests. Deliverables include the checkpoint, model card, data sheet, safety report, and a cost-latency profile so you know what it takes to run at scale.
Benefits to Businesses
Mu Sigma GenAI lifts throughput by automating multi-step creation and analysis across teams so more work ships with consistent quality, personalizes at scale by using retrieval and knowledge graphs to tailor outputs to products, segments, and policies in real time, accelerates speed to insight as agentic workflows draft, review, and publish in hours with evaluation harnesses that show what to trust, drives cost leverage by right-sizing models, reusing prompts and macros, and routing tasks to the cheapest viable path while tracking latency and GPU spend, and compounds knowledge reuse through muUniverse where domain facts, prompts, and results live so every project starts closer to done; net effect is faster cycles, higher adoption, lower unit cost, and a decision system that keeps getting smarter.
Risks Handled Upfront
Bias and Fairness
- Build diverse training and eval sets, including counterfactuals.
- Audit with metrics such as demographic parity, equalized odds, calibration, and subgroup error.
- Remediate using reweighting, prompt balancing, or post-processing.
- Track fairness KPIs over time and review impact with domain owners.
Bias and Fairness
- Build diverse training and eval sets, including counterfactuals.
- Audit with metrics such as demographic parity, equalized odds, calibration, and subgroup error.
- Remediate using reweighting, prompt balancing, or post-processing.
- Track fairness KPIs over time and review impact with domain owners.
Hallucination
- Ground generation with retrieval over verified sources, require citations, and score faithfulness.
- Run retrieval quality tests like precision at k and recall at k before release.
- Add uncertainty thresholds, refusal policies, and human escalation paths.
- Enable fallbacks like regenerate, switch model, or route to expert.
Hallucination
- Ground generation with retrieval over verified sources, require citations, and score faithfulness.
- Run retrieval quality tests like precision at k and recall at k before release.
- Add uncertainty thresholds, refusal policies, and human escalation paths.
- Enable fallbacks like regenerate, switch model, or route to expert.
μσ Gen AI Governance
- Operate with a policy store, model cards, data sheets, and decision logs.
- Register models, gate changes with approvals, and tier risks by use case.
- Red-team routinely, monitor drift and toxicity, and trigger incident playbooks when needed.
- Tie evaluation harnesses to business KPIs so “safe” also means “useful.”
μσ Gen AI Governance
- Operate with a policy store, model cards, data sheets, and decision logs.
- Register models, gate changes with approvals, and tier risks by use case.
- Red-team routinely, monitor drift and toxicity, and trigger incident playbooks when needed.
- Tie evaluation harnesses to business KPIs so “safe” also means “useful.”
Security and Privacy
- Detect and redact PII, enforce data minimization, and apply field-level encryption.
- Honor data residency, isolate tenants, and enforce least-privilege access with RBAC or ABAC.
- Use private networking, KMS key management, and a secrets vault.
- Maintain tamper-evident audit logs, DLP scans, and optional differential privacy.
Security and Privacy
- Detect and redact PII, enforce data minimization, and apply field-level encryption.
- Honor data residency, isolate tenants, and enforce least-privilege access with RBAC or ABAC.
- Use private networking, KMS key management, and a secrets vault.
- Maintain tamper-evident audit logs, DLP scans, and optional differential privacy.
IP and Compliance
- Scan licenses for third-party models, datasets, and code, and enforce usage terms.
- Track data and model lineage end to end and keep provenance where supported, including C2PA.
- Filter for copyrighted content, route sensitive outputs for human approval, and retain records for audit.
- Map controls to regimes like GDPR, HIPAA, PCI, and SOX, and honor deletion and retention policies.
IP and Compliance
- Scan licenses for third-party models, datasets, and code, and enforce usage terms.
- Track data and model lineage end to end and keep provenance where supported, including C2PA.
- Filter for copyrighted content, route sensitive outputs for human approval, and retain records for audit.
- Map controls to regimes like GDPR, HIPAA, PCI, and SOX, and honor deletion and retention policies.
Bias and Fairness
- Build diverse training and eval sets, including counterfactuals.
- Audit with metrics such as demographic parity, equalized odds, calibration, and subgroup error.
- Remediate using reweighting, prompt balancing, or post-processing.
- Track fairness KPIs over time and review impact with domain owners.
Bias and Fairness
- Build diverse training and eval sets, including counterfactuals.
- Audit with metrics such as demographic parity, equalized odds, calibration, and subgroup error.
- Remediate using reweighting, prompt balancing, or post-processing.
- Track fairness KPIs over time and review impact with domain owners.
Hallucination
- Ground generation with retrieval over verified sources, require citations, and score faithfulness.
- Run retrieval quality tests like precision at k and recall at k before release.
- Add uncertainty thresholds, refusal policies, and human escalation paths.
- Enable fallbacks like regenerate, switch model, or route to expert.
Hallucination
- Ground generation with retrieval over verified sources, require citations, and score faithfulness.
- Run retrieval quality tests like precision at k and recall at k before release.
- Add uncertainty thresholds, refusal policies, and human escalation paths.
- Enable fallbacks like regenerate, switch model, or route to expert.
μσ Gen AI Governance
- Operate with a policy store, model cards, data sheets, and decision logs.
- Register models, gate changes with approvals, and tier risks by use case.
- Red-team routinely, monitor drift and toxicity, and trigger incident playbooks when needed.
- Tie evaluation harnesses to business KPIs so “safe” also means “useful.”
μσ Gen AI Governance
- Operate with a policy store, model cards, data sheets, and decision logs.
- Register models, gate changes with approvals, and tier risks by use case.
- Red-team routinely, monitor drift and toxicity, and trigger incident playbooks when needed.
- Tie evaluation harnesses to business KPIs so “safe” also means “useful.”
Security and Privacy
- Detect and redact PII, enforce data minimization, and apply field-level encryption.
- Honor data residency, isolate tenants, and enforce least-privilege access with RBAC or ABAC.
- Use private networking, KMS key management, and a secrets vault.
- Maintain tamper-evident audit logs, DLP scans, and optional differential privacy.
Security and Privacy
- Detect and redact PII, enforce data minimization, and apply field-level encryption.
- Honor data residency, isolate tenants, and enforce least-privilege access with RBAC or ABAC.
- Use private networking, KMS key management, and a secrets vault.
- Maintain tamper-evident audit logs, DLP scans, and optional differential privacy.
IP and Compliance
- Scan licenses for third-party models, datasets, and code, and enforce usage terms.
- Track data and model lineage end to end and keep provenance where supported, including C2PA.
- Filter for copyrighted content, route sensitive outputs for human approval, and retain records for audit.
- Map controls to regimes like GDPR, HIPAA, PCI, and SOX, and honor deletion and retention policies.
IP and Compliance
- Scan licenses for third-party models, datasets, and code, and enforce usage terms.
- Track data and model lineage end to end and keep provenance where supported, including C2PA.
- Filter for copyrighted content, route sensitive outputs for human approval, and retain records for audit.
- Map controls to regimes like GDPR, HIPAA, PCI, and SOX, and honor deletion and retention policies.
Ready to build useful, reliable, and auditable GenAI?
