AI That Works
In the Real World.

Data quality audit, use-case prioritization, and ROI modeling before any development begins.

Bespoke AI applications built on your data and integrated into your existing workflows.

Model selection across OpenAI, Anthropic, Google, and open-source with domain-specific fine-tuning.

API-first integrations with your ERP, CRM, EHR, POS, and other existing systems.

Ingestion, cleaning, and transformation pipelines that keep AI models trained on fresh, accurate data.

Retrieval-augmented generation systems for knowledge bases, document Q&A, and search.

Autonomous AI agents for multi-step task completion, research, and workflow orchestration.

Latency, accuracy, and drift monitoring dashboards with alerting and auto-retraining triggers.

Workflow audit identifying the 20% of processes that represent 80% of automation value.

OCR, extraction, and classification for contracts, invoices, forms, and medical records.

Robotic process automation for structured web and desktop application interactions.

Conditional routing and approval automation with human-in-the-loop escalation paths.

Scheduled AI-generated reports delivered to stakeholders with narrative commentary.

AI triage, response drafting, and routing for customer service and internal communications.

Automated data ingestion from unstructured sources into structured business systems.

Zapier-to-enterprise workflow orchestration connecting every system in your stack.

Vulnerability testing for prompt injection, jailbreaking, and model extraction attacks.

PII exposure analysis, data lineage review, and remediation for CCPA/GDPR/HIPAA compliance.

Red-team exercises targeting AI model behavior under adversarial inputs.

Mapping of AI system gaps against Nevada SB 262, EU AI Act, and sector-specific regulations.

AI API endpoint hardening, rate limiting, authentication, and abuse prevention.

Third-party AI tool risk scoring for shadow AI use by employees.

Playbook development for AI system compromise, model failure, and data exposure events.

Prioritized remediation roadmap with timelines, owners, and verification testing.

Policies, accountability structures, and oversight mechanisms for your AI program.

Documented acceptable use policies for employees, contractors, and vendors.

Tiered data classification and handling rules that govern what data AI can and cannot access.

Model inventory, validation requirements, and periodic review cadence for deployed models.

Evidence packages for regulators across Nevada AI law, EU AI Act, NIST AI RMF, and others.

Training requirements, certification tracks, and acceptable tool lists for staff.

Procurement-stage AI risk questionnaires and vendor evaluation frameworks.

Quarterly board-level AI risk and compliance reporting templates.

Full-organization AI fluency curriculum covering concepts, tools, and responsible use.

Board and C-suite sessions covering AI strategy, competitive implications, and governance.

Department-level AI opportunity mapping and team-level adoption roadmaps.

Hands-on sessions for using ChatGPT, Copilot, Claude, and other LLM tools effectively.

Engineering and data science upskilling on MLOps, fine-tuning, and AI architecture.

Custom internal AI certification tracks with assessments and credentials.

Stakeholder communication plans and resistance management for AI rollouts.

Monthly lunch-and-learns, curated AI news digests, and quarterly tool update sessions.

Scored evaluation of data, talent, infrastructure, and organizational readiness for AI.

Intelligence on how your direct competitors are deploying AI and where you have gaps.

ROI-ranked AI use case shortlist with implementation complexity and time-to-value estimates.

Board-ready financial models and strategic narratives for each prioritized AI initiative.

Build vs. buy vs. partner recommendations and AI org design for your maturity level.

RFP development, vendor evaluation, and negotiation support for AI platform decisions.

12–24 month phased roadmap with milestones, dependencies, and success metrics.

Ongoing advisory relationship with quarterly strategy sessions and market updates.

Full audit of current AI costs across all vendors, tools, and internal usage patterns.

Real-time dashboards tracking token consumption, cost per transaction, and cost per user.

Systematic prompt engineering to reduce token usage without degrading output quality.

Task-by-task model routing to match capability requirements with cost-appropriate models.

Semantic caching, response deduplication, and retrieval optimization to cut redundant calls.

Monthly spend forecasting with anomaly alerts before costs exceed approved thresholds.

Volume commitment analysis, reserved capacity planning, and multi-vendor arbitrage.

Prioritized list of optimization actions with estimated savings for each initiative.