A 3-to-4-week fixed-price sprint that audits data infrastructure, benchmarks lightweight Small Language Models (SLMs) and vision encoders on your live datasets, models scale cost-per-query, and delivers a de-risked go/no-go production roadmap before you write production code.
We eliminate the common pitfalls behind the 90% AI pilot failure rate by stress-testing data hygiene, API throughput, and unit economics on representative workloads—proving technical viability before committing engineering headcount.
Benefits
Rapid Feasibility & Scoping
Validate technical viability and data quality before writing production code or committing capital.
Overcome the 90% AI Productivity Gap
Provide executive-level clarity and defensible unit economics to ensure initiatives avoid becoming part of corporate AI projects that fail to generate measurable returns.
Executive Alignment & ROI Modeling
Secure stakeholder alignment with transparent token-cost modeling and unit economics projections.
Security & Governance Pre-emption
Establish strict data ingress boundaries, anonymization protocols, and compliance guardrails before deployment.
E-Commerce & Digital Marketplaces
Automated product cataloging, multi-modal image-to-taxonomy encoding, and marketplace listing optimization.
Enterprise Retail & Commercial Operations
Sales conversation intelligence, objection profiling, and revenue optimization pipelines.
Specialized Manufacturing & Chemicals
Centralized document indexing, multilingual retrieval, and technical specification Q&A.
PropTech & Transactional Platforms
Modernizing legacy data layers, decomposing monoliths, and managing complex real estate listings.
How do you ensure our proprietary data remains secure during the readiness sprint?
We operate under a strict Zero Public Training guarantee, ensuring proprietary schemas, business logic, and test data are never used to train public models. All evaluations utilize sanitized sandboxes, anonymized metadata, or local endpoints