Technical transformation that increases enterprise value.

Business situation

A late-stage healthcare AI company had meaningful demand and proprietary clinical-workflow data, but fragmented architecture, uneven release discipline, vendor dependency, weak observability and evaluation, and unclear platform ownership constrained safe clinical deployment and created material scale and diligence risk.

The board, investors, and executive team needed to prove that agents could operate safely in live clinics, earn healthcare-system trust, and scale on an architecture the company could govern and own.

Business objective

Deploy governed agents into clinical production while converting proprietary data and workflow advantages into an investor-ready platform with durable operating control and internal ownership.

Two governed agents reached clinical production and earned healthcare-system trust. Under Bottega8’s interim CTO, principal AI engineering, and Agentic AI architecture leadership, the company also became auditable, investor-ready, and able to own the system as durable IP.

Business situation

A late-stage financial-data company had trusted institutional data, defensible IP, and strong engineers. Fragmented product lines, acquired platforms, overlapping vendors, manual reconciliation, unclear ownership, and legacy cloud architecture were consuming capital and slowing production.

Leadership needed one evidence base connecting the assets worth protecting to the work that required consolidation, automation, restructuring, investment, or discontinuation.

Business objective

Release structural cost while protecting differentiated data assets, improving production economics, and creating a credible platform and AI modernization sequence.

The engagement released approximately $350,000 per month in structural cost, improved the platform investment model, and gave leadership a sequenced plan for consolidation, refactoring, automation, and ownership.

Business situation

A healthcare AI organization needed multiple clinic launches and new agent workflows from a constrained engineering team without increasing regression, PHI, security, or architectural risk.

The existing delivery system could not create the required throughput safely; accelerating code generation alone would have amplified review, integration, and release risk.

Business objective

Increase completed delivery with the same team while preserving human authority over architecture, security, merge decisions, PHI controls, and production release.

Completed story points per sprint increased approximately 2.5× across six sprints. Defect, first-draft implementation, and product-decomposition cycles also improved directionally without changing the team or transferring production authority to agents.

Business situation

An investor-backed marketplace was approaching failure. Board-appointed Bottega8 ran technical and commercial forensics across the team, systems, process, IP, product-market fit, positioning, GTM, and sales motion, then took integrated operating leadership across technology, product, and revenue.

The board and executive team needed an immediate company truth: what customers still valued, what could be preserved, what had to change, and whether a credible operating and commercial recovery was possible.

Business objective

Protect the company’s differentiated assets, restore product and engineering execution, reposition the offer for enterprise demand, rebuild the GTM and sales motion, and create a credible path to an exit.

During an intensive 10-week Bottega8-led engagement, the company realigned its product, technology, operations, positioning, and sales motion, relaunched against market demand, and restored a viable operating trajectory. Bottega8 then helped guide the company to an exit within the following quarter.

Business situation

A data company was carrying expensive third-party dependencies, duplicated vendor spend, organizational cost overruns, and brittle production paths that affected accuracy and release speed. Bottega8 traced the economics into the architecture and operating model, then directed the changes required to remove structural cost.

Leadership could see rising cost and slower delivery, but the financial symptoms were distributed across vendor contracts, code dependencies, data workflows, team boundaries, and production ownership.

Business objective

Reduce structural cost, remove critical third-party dependencies, improve data accuracy, and shorten the path from change to production without weakening the differentiated platform.

The company removed approximately $2 million in structural cost, improved technical control over data accuracy and release speed, revitalized the business, and entered its next institutional funding process from a stronger operating position.

Business situation

Document-heavy enterprise workflows lose essential meaning when PDFs, spreadsheets, presentations, contracts, tables, graphs, and supporting records are reduced to generic text chunks.

A plausible answer was insufficient: every conclusion needed to preserve page context, evidence, domain structure, and the ability to replay and challenge the decision.

Business objective

Build a production AI and machine-learning system that reduces complex document review from weeks to minutes without separating an answer from its evidence or accountable reviewer.

One to two weeks of elapsed review across stakeholders became minutes of AI analysis with human-in-the-loop control. End-to-end workflow throughput improved an estimated 7–14×, depending on corpus size and workflow.