Every engagement is measured against a single question: did this create material, measurable business value? Below is a selection of what that looks like in practice. Client names are withheld unless explicit permission is granted.
A top-25 U.S. bank had attempted two internal AI initiatives over three years, investing more than $28M in each, with neither reaching production. Senior leadership brought us in to conduct an honest post-mortem and provide a path forward.
Our diagnostic revealed that both prior programs had failed not because of inadequate model selection or engineering talent, but because of fundamental data infrastructure problems. Thirteen core data assets required to power the AI systems were siloed across four legacy systems, inconsistently defined, and incompletely reconciled. No amount of model sophistication could overcome that foundation.
We restructured the program around a data-first approach — spending the first four months building the infrastructure before touching model development. This required convincing senior leadership that pace was less important than sequence, and that another fast start would produce another failure. That conversation was difficult. It was also correct.
In month five, model development began in earnest. By month fourteen, four production AI systems were live: a credit underwriting model, a fraud detection system, a customer churn prediction engine, and an internal document intelligence tool for compliance. All four passed their first Model Risk Management review without findings.
A 12-hospital integrated health system faced a crisis that most healthcare leaders recognize: physician burnout driven substantially by administrative documentation burden. Physicians were spending an average of 3.1 hours per day on documentation — time that should have been spent on patients.
Previous attempts to implement commercial documentation AI had achieved 40% adoption before physicians abandoned the tools, citing accuracy problems and workflow friction that created more work than it saved. The clinical informatics team brought us in specifically because our implementation approach emphasizes adoption engineering alongside model engineering.
We began by shadowing physicians across four specialties for two weeks — not interviewing them, but observing how they actually worked, where documentation occurred in their workflow, and what specifically they found friction-generating about the prior tools. This produced a behavioral map that became the design foundation for everything that followed.
The system we built uses a fine-tuned clinical language model, integrated directly into the EHR's existing interface, with specialty-specific prompting systems that produce note drafts matching each physician's documented style. A human review step is preserved for all final notes — the system assists, it does not replace clinical judgment.
A mid-market specialty retailer discovered, through their own consumer research, that when their target customers asked AI assistants for product recommendations, a legacy competitor was cited in 80% of responses. Their own brand appeared in fewer than 12% of AI-generated responses — despite being rated higher by customers who had actually used both brands.
The disconnect was not a product quality problem. It was an AI visibility problem. The competitor had decades of inbound link authority and structured content that had been indexed and absorbed by AI training pipelines. The client's digital presence, while aesthetically superior, lacked the structured content signals that AI systems use to evaluate credibility and relevance.
Our AI Visibility program began with a comprehensive audit mapping every dimension of their AI search presence: citation frequency across platforms, sentiment accuracy, product representation quality, and structural content gaps. We identified 14 specific gaps in their content architecture that were directly suppressing AI citation rates.
The remediation program addressed each gap systematically: structured product data in schema markup, category-level FAQ content written for AI extraction, an llms.txt file providing AI systems with authoritative brand information, and a coordinated effort to secure structured third-party mentions from review platforms with high AI index authority. Within 90 days, citation share had reversed.
A 600-attorney global law firm identified legal research as their single highest-cost inefficiency — associates spending 30-40% of billable time on research tasks that, in principle, could be substantially automated. The challenge was designing a system that met the firm's non-negotiable requirements: citation accuracy, jurisdictional precision, and a human attorney review step that maintained professional accountability.
We designed a multi-agent research system comprising four specialized agents: a query decomposition agent that breaks complex legal questions into structured sub-queries, a parallel retrieval agent that searches across case law databases, regulatory filings, and statute libraries across 14 jurisdictions simultaneously, a synthesis agent that compiles and cross-checks findings, and a citation verification agent that validates every reference before it reaches an attorney's screen.
The system is explicitly designed to augment attorney judgment, not replace it. Every output is formatted as a structured research memorandum with confidence levels, jurisdictional flags, and explicit uncertainty markers — giving attorneys the context they need to review and rely on the research with appropriate professional diligence.
The 99.1% citation accuracy rate was achieved through an intensive validation program in which 200 randomly selected research outputs were manually verified by senior associates. This validation process also produced the training data used to improve the citation verification agent's performance over successive iterations.
If you have a serious AI program to build or an existing one that isn't performing, we'd like to hear about it. Our initial briefings are confidential and without obligation.