Unlock the Future with
Generative AI
Bring it to Life
Innovate, Create, and Scale with Generative AI
Your ideas—enhanced. Innovation—faster. Productivity—elevated. Creativity—unleashed.
At Ontrac Solutions, we help you harness the power of Generative AI and cutting-edge AI technologies to drive real business transformation. Plus, with the exclusive Google Cloud GenAI consumption credit program, you can save while building your next AI-powered solution.
Let’s explore the possibilities together.
Navigating AI with Confidence
AI: Art of the Possible
We empower your company to explore the untapped potential of artificial intelligence, discovering new opportunities to innovate, optimize, and succeed.
Our Services
Building a Roadmap to AI Success
An AI capability assessment delivers clarity on your organization’s readiness for AI and ensures you’re set up for success.
ROI & Cost-Benefit
Investing in an AI assessment delivers measurable returns by ensuring strategic, cost-effective, and high-impact AI implementation.
Efficiency Gains with AI
AI enhances business efficiency by automating processes, optimizing workflows, and reducing operational bottlenecks. By leveraging AI-powered tools, companies can significantly cut down on manual labor, minimize errors, and accelerate decision-making.
Revenue Growth with AI Implementation
AI drives revenue growth by enhancing decision-making, personalizing customer experiences, and optimizing operations. AI-powered insights help businesses anticipate trends, boost customer retention, and scale more effectively, ensuring long-term profitability in a competitive market.
Payback Period for AI Investment
Investing in AI pays off—fast. With the right strategy, businesses can recover their AI investment within 1 to 3 years, unlocking efficiency, reducing costs, and driving revenue growth. By focusing on high-impact use cases, you can accelerate value and see real financial gains sooner.
Responsible AI
Responsible AI ensures that artificial intelligence systems are ethical, transparent, and fair, minimizing bias and ensuring accountability in decision-making. It prioritizes privacy, security, and compliance, fostering trust while aligning AI solutions with human and societal values.
Let’s Talk AI
Book a complimentary consultation and we’ll map your fastest route from AI pilot to production.
Related Insights
Frequently Asked Questions
Get answers to common questions about our GenAI consulting services.
Most engagements start with a 4-week AI Readiness Audit covering data, infrastructure, governance, and use-case prioritization, followed by a 90-day production pilot. The deliverable is a working AI capability tied to a measurable business metric — not a slide deck. Ontrac has run this pattern with mid-market enterprises across financial services, healthcare, and PE-backed portfolio companies.
We score candidate use cases on four dimensions: data readiness, business-value defensibility, technical feasibility, and change-management risk. The highest-ROI starting points for most mid-market enterprises are document summarization, support deflection, and proposal automation — not customer-facing chatbots, which carry the most risk.
Traditional AI consulting focused on predictive ML models trained on customer data. GenAI consulting focuses on foundation-model adaptation: retrieval-augmented generation, fine-tuning, and agentic workflows. The skill set, infrastructure, and governance challenges (hallucination, IP leakage) are different, and we staff each engagement accordingly.
Budget ranges we typically see: $150K–$350K for a single production pilot including the readiness audit, vendor and tooling costs, and 90 days of build. Recurring spend after year one (cloud, model API, and maintenance) usually runs $40K–$120K per pilot annually. Be wary of vendors that lead with seven-figure year-one quotes.
All of them, depending on the use case. We don’t lock clients into a single vendor. Decision factors include data sensitivity, latency tolerance, cost-per-token at expected volume, and fine-tuning needs. Reasoning-heavy tasks typically use frontier models, while cost-sensitive bulk workloads often run on open-source models.
Every engagement starts with a data-classification audit. Sensitive data (PHI, PII, IP) is processed inside the client’s cloud tenant rather than via public APIs. We deliver a documented AI governance framework covering use-case approval, monitoring, and human-in-the-loop checkpoints — pre-empting the audit questions that block production deployments.
