Our Services
We validate AI feasibility against your real workflows before engineering commits. Generative AI, computer vision, machine learning, and predictive analytics, de-risked and production-ready.
74% of companies struggle to extract business value from AI investments. 80% of enterprise AI projects fail to move from pilot to production. The pattern is consistent: teams select models based on demos rather than real workflow testing, skip governance planning, and commit engineering resources before validating that the AI approach actually solves the business problem.
The cost isn't just failed projects — it's the engineering time consumed, the organizational credibility burned, and the competitive window missed while teams rebuild from avoidable mistakes.
Planorama exists at this exact friction point. We validate AI feasibility against your actual workflows, data, and constraints before your engineering team commits a single sprint. When AI is the right answer, we prove it. When it isn't, we tell you before it's expensive.
We evaluate LLM capabilities against your actual use cases — not synthetic benchmarks. This includes RAG architecture design and testing with your real data, evaluation framework development that measures what matters to your business, prompt engineering for production reliability, and integration planning that accounts for latency, cost, and compliance requirements. We test whether the models you're considering actually fit the workflows you need them for, and surface integration challenges before engineers write production code.
From image classification and object detection to visual inspection systems, we prototype computer vision solutions against your actual image data and operational conditions. We evaluate model accuracy under real-world variability — lighting conditions, image quality, edge cases — and validate that the system performs reliably enough for production deployment. The goal is always the same: de-risk before engineering commits.
Classification, forecasting, anomaly detection, recommendation systems — we evaluate whether ML approaches are the right solution for your specific problem, test model performance against your data, and validate that the predictions are accurate and actionable enough to drive business decisions. Not every problem needs machine learning. When it does, we ensure the approach is validated before your team builds around it.
For organizations evaluating where AI fits in their product roadmap, we provide structured AI readiness assessments, technology selection guidance, governance framework design, and build-vs-buy analysis. We help teams distinguish between AI opportunities that will deliver measurable value and AI features that look impressive in a demo but collapse under real-world conditions.
Our AI prototyping and feasibility work is accelerated by FlashQuery, Planorama's self-hosted, model-agnostic AI middleware platform. FlashQuery enables rapid evaluation across multiple LLMs, supports RAG prototyping with your actual data, and provides the testing infrastructure needed to validate AI approaches quickly and rigorously.
Because FlashQuery is model-agnostic and designed for self-hosted deployment, it gives our team — and yours — the flexibility to evaluate any model without vendor lock-in. Open-source models, commercial APIs, fine-tuned variants — FlashQuery supports them all, so recommendations are based on what works best for your problem, not what a platform vendor is incentivized to sell.
We're not partnered with AWS, Anthropic, Google, OpenAI, or any AI platform vendor. We have no financial incentive to recommend one technology over another. Our recommendations are based entirely on what solves your problem most effectively — whether that's an open-source model running on your infrastructure, a commercial API, or a decision that AI isn't the right approach at all.
AI investment decisions made without feasibility validation create compounding costs. Engineering teams build around assumptions that haven't been tested. Product roadmaps commit to AI features that may not perform in production. Organizations lose months — and the competitive window — discovering problems that structured validation would have surfaced in weeks.
For CTOs and engineering leaders, this means fewer failed AI experiments, reduced rework when AI features reach production, and engineering teams focused on building validated solutions rather than debugging unfounded assumptions.
For product leaders, this means AI features that actually ship — not perpetual pilots that consume roadmap space without delivering value. Validated feasibility translates directly into confident sprint planning and predictable delivery.
For CEOs and founders, this means competitive advantage without runaway AI costs. Structured validation de-risks the investment, compresses the timeline from concept to production, and ensures that AI capabilities align with business objectives rather than technology trends.