Berry Development / AI systems consulting

AI engineering for revenue-critical business systems.

I help teams turn messy workflows, stalled AI pilots, and manual handoffs into dependable software that improves growth, margin, and operating speed.

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Phase01

Workflow selected for business impact before any model choice.

Phase02

Production boundary designed around tools, data, people, and review.

Phase03

System shipped with measurement, observability, and ownership.

Eric Berry

AI engineer, consultant, and builder of enterprise-grade software systems.

Operating model

Business goal

DataToolsPeopleReview

Revenue system

Positioning

Strategy that ends in shipped software.

Consulting, architecture, and implementation stay connected from the first call.

AI assistants that use real business tools

Workflow automation with reviewable handoffs

Data access patterns that keep context trustworthy

What Berry Development does

Turn AI from scattered experiments into operating leverage.

I help teams find the workflows where AI can create real business value, then design, build, and deploy the systems that make those workflows reliable enough for daily use.

AI Integration Strategy

Map business goals to practical AI systems, prioritize the highest-leverage workflows, and define the production path before tooling sprawl sets in.

Opportunity map
Implementation roadmap
Risk and data review

Agentic Workflow Automation

Design and ship AI-assisted processes for research, triage, reporting, operations, support, and back-office work where cycle time matters.

Human-in-the-loop flows
Tool and API integrations
Operational guardrails

Internal AI Products

Build reliable internal applications that package models, data, approvals, and observability into software your team can use every day.

Custom interfaces
Secure data access
Deployment and maintenance

Working principles

Clean systems, clear ownership, visible business impact.

Revenue and growth first, model choice second.

Small production systems beat sprawling demos.

Human review stays explicit where judgment matters.

Every integration needs ownership, observability, and a rollback path.

Selected work

Practical AI integration patterns for real teams.

View case studies

Revenue Operations Team

From scattered requests to an AI-assisted intake and reporting system

Reduced manual handoffs and made leadership reporting repeatable.

Designed the workflow, connected operational data, and shipped a pragmatic assistant that produces reviewable summaries instead of loose chat output.

Founder-Led Services Company

AI automation roadmap for growth without adding headcount

Created a prioritized backlog tied to revenue impact and operational risk.

Audited repeated work, data readiness, customer touchpoints, and internal bottlenecks to identify where automation could actually move the business.

Blog platform

Publishing is API-managed from day one.

Posts are stored in a writable JSON content file for the Docker MVP, exposed through authenticated API routes, and ready to be swapped to a headless CMS when you pick the long-term backend.

GET /api/healthGET /api/postsPOST /api/postsPATCH /api/posts/:slugDELETE /api/posts/:slug

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