You’ve Built GenAI That Works in the Demo.
Now Make It Work in Production.
Leadership approved the GenAI initiative. Your model shows promise. Then you look at the integration points - Snowflake connections, HIPAA compliance, 500K daily transactions, existing reporting pipelines that can't break
The gap between "approved" and "deployed" is where we work. Data infrastructure, integration architecture, and production planning that turns prototypes into systems that actually run.
The gap between "approved" and "deployed" is where we work. Data infrastructure, integration architecture, and production planning that turns prototypes into systems that actually run.
Recent technical work
Migrated 15-year-old Perl ETL to Python. 500K records/day, healthcare data, zero downtime
Built HIPAA-compliant data lake supporting LLM + analytics workloads
Automated QBR pipeline: 3 days of manual work v/s 2 hours automated
Technical Consulting for GenAI Infrastructure
We Deliver Working Infrastructure, Not PowerPoints
Infrastructure Audit & Implementation Roadmap
$2,000
Big 4 equivalent: $15,000+
Real technical depth at fixed cost
Technical audit of your data pipelines, integration points, API contracts,
data quality checks. Identification
of where failures happen, why batch
jobs miss SLAs, what breaks under
load.
data quality checks. Identification
of where failures happen, why batch
jobs miss SLAs, what breaks under
load.
-> Technical audit report
(not generic findings — specific to your stack)
-> Reference architecture diagrams on how to connect your LLM to existing systems
-> Infrastructure-as-code samples (Terraform/CloudFormation you can deploy)
-> Implementation roadmap with dependencies, risk assessment, rollback strategies
(not generic findings — specific to your stack)
-> Reference architecture diagrams on how to connect your LLM to existing systems
-> Infrastructure-as-code samples (Terraform/CloudFormation you can deploy)
-> Implementation roadmap with dependencies, risk assessment, rollback strategies
When this makes sense: Before committing to large GenAI projects. When you need to know if your infrastructure can actually support what's planned.
GenAI Integration Architecture & Production Blueprint
$3,000
Big 4 equivalent: $20,000+
Production-ready designs your team can implement
Integration design for connecting your
LLM to production databases and
services. Data flow architecture, production operations plan, security/compliance blueprint for handling PII and audit requirements.
LLM to production databases and
services. Data flow architecture, production operations plan, security/compliance blueprint for handling PII and audit requirements.
-> Integration architecture design
audit with API specifications (OpenAPI format)
-> Data flow diagrams showing source systems >> preprocessing >> LLM >> downstream consumers
-> Infrastructure templates (Terraform/CloudFormation
ready to deploy)
-> Production runbook: monitoring,
alerting, failover procedures,
rollback plan
audit with API specifications (OpenAPI format)
-> Data flow diagrams showing source systems >> preprocessing >> LLM >> downstream consumers
-> Infrastructure templates (Terraform/CloudFormation
ready to deploy)
-> Production runbook: monitoring,
alerting, failover procedures,
rollback plan
When this makes sense: When your prototype works but nobody's sure how to operationalize it. When you need production architecture, not another strategy deck.
Business Metrics & ROI Instrumentation
Plan
$5,000
Big 4 equivalent: $35,000+
Measurement frameworks that connect to actual P&L
Metrics definition for measuring business impact (not just accuracy/latency). Instrumentation plan showing where to add tracking, what events to capture. ROI model connecting technical metrics to dollar impact.
-> Metrics specification document (what to measure, where, how)
-> Working ROI calculator
(Excel / Python) with your actual
numbers
-> Instrumentation plan
your engineers can
implement
-> Executive dashboard
that your finance team will understand
-> Working ROI calculator
(Excel / Python) with your actual
numbers
-> Instrumentation plan
your engineers can
implement
-> Executive dashboard
that your finance team will understand
When this makes sense: When leadership asks "what's the business case?" and you're showing technical metrics. When you need finance to understand why this matters.
Technical Problems We've Actually Solved
We're showing you this because every company says they "build infrastructure."
Challenge: 15-year-old Perl scripts processing patient records. Original engineer gone. Any change risked breaking downstream systems. Business needed faster processing.
Technical work: Reverse-engineered Perl transformations. Built Python replacement with Airflow orchestration. Parallel-run validation for 2 weeks. Cutover with 1-hour rollback window.
Result: Processing time 8 hours → 45 minutes. Team can add data sources in days now. Zero downtime during migration.
Stack used: Python, Apache Airflow, PostgreSQL, AWS S3
Timeline: 12 weeks
Technical work: Reverse-engineered Perl transformations. Built Python replacement with Airflow orchestration. Parallel-run validation for 2 weeks. Cutover with 1-hour rollback window.
Result: Processing time 8 hours → 45 minutes. Team can add data sources in days now. Zero downtime during migration.
Stack used: Python, Apache Airflow, PostgreSQL, AWS S3
Timeline: 12 weeks
Challenge: Data scattered across 6 systems. No way to safely test LLMs on real member data. HIPAA compliance requirements unclear for AI workloads.
Technical work: Built AWS data lake (S3 + Glue + Athena). Created de-identification pipeline. Set up audit logging for all data access. IAM policies + encryption for compliance.
Result: Centralized platform supporting 3 LLM use cases. Passed HIPAA audit. Data prep time weeks → hours.
Stack used: AWS (S3, Glue, Athena, KMS), Python, Terraform
Timeline: 10 weeks
Technical work: Built AWS data lake (S3 + Glue + Athena). Created de-identification pipeline. Set up audit logging for all data access. IAM policies + encryption for compliance.
Result: Centralized platform supporting 3 LLM use cases. Passed HIPAA audit. Data prep time weeks → hours.
Stack used: AWS (S3, Glue, Athena, KMS), Python, Terraform
Timeline: 10 weeks
Challenge: QBRs required 3 engineers × 2 days extracting data, formatting slides. Reports always 1 week stale by presentation time.
Technical work: Built automated ETL from 4 data sources. Created Looker dashboards with drill-downs. Automated slide generation (Python + Google Slides API). Daily refresh schedule.
Result: QBR prep 3 days → 2 hours. Dashboards updated daily. Engineering team freed up for product work.
Stack used: Python, Airflow, Looker, BigQuery, Google Workspace APIs
Timeline: 6 weeks
Technical work: Built automated ETL from 4 data sources. Created Looker dashboards with drill-downs. Automated slide generation (Python + Google Slides API). Daily refresh schedule.
Result: QBR prep 3 days → 2 hours. Dashboards updated daily. Engineering team freed up for product work.
Stack used: Python, Airflow, Looker, BigQuery, Google Workspace APIs
Timeline: 6 weeks
Why Engineering Leaders Choose This Approach
Enterprise-caliber insights without Big 4
overhead and timelines
overhead and timelines
Business-first methodology that prioritizes ROI
over technical novelty
over technical novelty
We've operated this infrastructure
in production, not just designed it
in production, not just designed it
You get working code and
reference implementations
reference implementations
Fixed scope, fixed timeline, fixed price -
before we start
before we start
Start Today
FAQs
- How does this compare to Big 4 consulting?
We deliver the same enterprise-grade strategic insights as major consulting firms, but with startup agility and accessible pricing. Our methodology eliminates the overhead while maintaining the rigor that executives expect.
- Why are your rates significantly lower than traditional consultants?
Our mission is reducing friction for strategic AI adoption. We’ve designed streamlined engagements that focus on high-impact deliverables rather than extensive documentation. This isn’t discount consulting – it’s efficient consulting.
- What level of executive engagement do you provide?
Every engagement includes C-suite presentation materials, board-ready business cases, and direct access to our strategic advisors. We work at the executive decision-making level, not the technical implementation level.
- How do you ensure enterprise-quality outputs in rapid timeframes?
Our frameworks are built from 100+ enterprise AI implementations. We focus on proven methodologies and business impact patterns rather than starting from scratch for each engagement.
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