LangChain / LangGraph Agentic RAG Pipelines SCADA Integration Zero Hallucination Tolerance Grid Intelligence Multi-Agent Workflows LangSmith Observability Human-in-the-Loop Deterministic Execution LangChain / LangGraph Agentic RAG Pipelines SCADA Integration Zero Hallucination Tolerance Grid Intelligence Multi-Agent Workflows LangSmith Observability Human-in-the-Loop Deterministic Execution
Agentic AI for Critical Infrastructure

Production-Grade AI for
Energy, Grid
& Logistics

We build AI systems for industries where the data is old, the stakes are high, and the margin for error is zero.

Stop running fragile pilots. We build deterministic, high-throughput LangChain architectures and multi-agent RAG pipelines that turn fragmented industrial data into real-time operational intelligence.

Book an Infrastructure Assessment View Technical Framework
Grid Infrastructure
Grid Infrastructure
Live Monitoring
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Albert Paramito
LinkedIn
About the Founder

Albert Paramito

Founder & CEO, Syntropi.io

30 years across commercial real estate acquisitions, capital markets, and hyperscale infrastructure development — with hands-on experience originating data center sites, evaluating BESS deployments, and navigating PJM, ERCOT, and MISO interconnection queues. Syntropi is built on that operational depth: not AI theory, but production systems built for the environments where reliability is non-negotiable.

30+Years infrastructure experience
3ISO markets: PJM, ERCOT, MISO
$2B+In assets evaluated & originated
GW+Data center & BESS pipeline managed
12+
Enterprise deployments
60–80%
MTTR reduction on deployment
3 wks
From assessment to blueprint
0
Hallucinations in production
100%
Auditable agent decisions
The Reality

Industrial assets don't tolerate hallucinations.

Modern enterprises sit on vast oceans of highly fragmented, multi-modal data — real-time SCADA sensor logs, geospatial infrastructure layers, unstructured PDF asset manuals, and regulatory grid codes. Naive chatbots break when exposed to any of it.

We bridge the gap between legacy operational technology and modern LLM orchestration, building stateful, auditable AI frameworks that optimize asset uptime, streamline supply chain routing, and automate complex compliance reporting.

100%
Auditability over every agent decision path
3wk
From architecture audit to blueprint delivery
0
Hallucination tolerance in mission-critical workflows
12wk
MVP to production-ready deployment timeline
Core Capabilities

Three Pillars of the Offering

A full-stack AI engineering framework purpose-built for high-stakes industrial environments.

Full Technical Detail
01 / 03
Industrial Multi-Modal RAG Pipelines
Replace generic document processing with a framework built to ingest engineering schematics, SCADA logs, and asset manuals. Hybrid dense + sparse vector search preserves formula context and multi-variable operational parameters through tokenization.
LangChainPgvectorQdrantBM25Unstructured
02 / 03
Deterministic LangGraph Agentic Workflows
Stateful, multi-agent runtimes with LangGraph ensure deterministic execution paths. Intent-classification routers safely delegate tasks to domain agents. RAG Triad Guardrails validate context relevance, groundedness, and answer relevance at every state node.
LangGraphStateGraphHITLGuardrails
03 / 03
Real-Time Tool Integration & Data Backhaul
Turn the LLM into a decision-making engine by wrapping SQL databases, SCADA APIs, and live asset streams as secure LangChain Tools. Satellite and edge-optimized for remote substations and constrained-bandwidth environments.
SCADA APISQL ToolsEdge/SatelliteAWS/Azure
Explore Full Technical Framework
Business Outcomes

What Changes After Deployment

Measurable operational improvements across asset management, compliance, and decision-making speed.

Reduced Mean Time to Repair
Field technicians instantly query 30 years of maintenance history and technical schematics via natural language to diagnose equipment failures in seconds — not hours. No more hunting through file servers.
Automated Compliance & Reporting
Reduce the time to compile complex regulatory compliance paperwork, environmental impact statements, and supply chain bottleneck reports from days to minutes. Agents handle the aggregation; humans approve the output.
De-Risked AI Implementation
By using a stateful graph architecture instead of a naive chatbot loop, your enterprise gains 100% auditability over how the AI agent arrived at any conclusion or recommendation — essential for regulatory and liability exposure.
Engagement Methodology

How We Deliver

A phased, milestone-driven approach from data audit to full production deployment in 12 weeks.

01
Weeks 1–3
Architecture & Data Audit
Audit existing data sources — maintenance logs, PDF schemas, GIS layers. Design the vector DB schema, embedding strategy, and metadata filtering taxonomy for your specific operational context.
02
Weeks 4–8
LangGraph & Tool Construction
Develop the core LangGraph state machine. Build custom toolsets connecting the LLM to real-time telemetry or logistics APIs. Implement strict system prompts, routing agents, and RAG Triad self-correction guardrails.
03
Weeks 9–12
Evaluation & Deployment
Instrument the entire pipeline using LangSmith for prompt debugging, latency tracing, and token cost optimization. Deploy into a secure cloud environment inside your enterprise perimeter.
Technical Execution Stack
LangChainLangGraphLangSmith PgvectorQdrantPinecone TruLensUnstructured.ioCamelot AWSAzurePrivate VPC

Ready to move from fragile pilots to production intelligence?

Our Infrastructure Assessment is the lowest-friction entry point — 3 weeks, a flat fee, and a complete architectural roadmap your team can act on immediately.

Book an Infrastructure Assessment View Pricing
Technical Consulting Offering

Agentic RAG & Grid Intelligence Frameworks

We build AI systems for industries where the data is old, the stakes are high, and the margin for error is zero.

Modern industrial, energy, and logistics enterprises sit on vast oceans of highly fragmented, multi-modal data — ranging from real-time SCADA sensor logs and geospatial infrastructure layers to unstructured PDF asset manuals and regulatory grid codes. We build the AI systems that make that data operational.

3
Core engineering pillars: RAG pipelines, LangGraph workflows, real-time tool integration
12wk
From data audit to production-ready deployment
0
Hallucination tolerance in mission-critical decision paths
100%
Auditability over every agent state and recommendation
Pillar 01 / 03
Data Ingestion & Embedding
Industrial Multi-Modal RAG Pipelines

We replace generic document processing with an engineering framework built to ingest complex engineering documents, schematics, and legacy asset manuals. The result is a vector infrastructure that can accurately answer queries across decades of operational data.

LangChainPgvectorQdrantPineconeBM25Unstructured.ioCamelot
Start with an Assessment
Advanced Document Parsing

LangChain document loaders integrated with layout-aware parsing via Unstructured.io and Camelot extract text, tables, and hierarchical metadata from complex asset manuals and grid blueprints — including wiring diagrams and multi-page schematics.

Context-Preserving Chunking

Semantic chunking strategies ensure formulas, wiring diagrams, and multi-variable operational parameters do not lose context during tokenization. Standard chunking breaks industrial documents; our approach treats them as structured data.

Hybrid Search Vector Infrastructure

A dual-engine retrieval pipeline combining dense vector search (Pgvector or Qdrant for semantic alignment) with sparse keyword search (BM25 for precise asset serial numbers, error codes, and exact technical terminology). This hybrid approach outperforms either strategy alone on industrial data.

Metadata Filtering Taxonomy

Custom metadata schemas enable filtering by asset type, maintenance period, regulatory jurisdiction, or equipment serial range — turning raw retrieval into a precise, context-aware query engine for field operators and compliance teams.

Intent-Classification Routing Agents

Inspect user queries and safely delegate tasks to specific domain agents — routing a "substation failure" query to the maintenance logs vector database vs. a real-time SCADA API — without exposing the agent to both simultaneously.

RAG Triad Guardrails

Automated evaluation steps validate three dimensions at every graph node: Context Relevance (did the vector DB retrieve relevant info?), Groundedness (is the LLM response strictly derived from retrieved documents?), and Answer Relevance (does the output safely answer the engineer's prompt?).

Human-in-the-Loop (HITL) Interventions

StateGraph checkpoints pause execution and require human approval before triggering critical external actions — drafting a formal regulatory grid-deviation report, issuing an automated vendor dispatch, or modifying operational setpoints.

Self-Correction Loops

When a guardrail fails, the graph re-routes to a self-correction subgraph that retrieves additional context, re-evaluates groundedness, and either returns a corrected response or escalates to a human reviewer — never silently producing a hallucinated output.

Pillar 02 / 03
Agentic Workflow Architecture
Deterministic LangGraph Agentic Workflows

For critical infrastructure and logistics, LLM hallucinations are a liability — not a tolerable edge case. We build stateful, multi-agent runtimes using LangGraph to ensure deterministic execution paths where every decision is traceable, auditable, and reversible.

LangGraphStateGraphHITLTruLensLangSmith
Discuss Your Architecture
Pillar 03 / 03
Real-Time Integration
Real-Time Tool Integration & Data Backhaul

Moving past static text retrieval, we turn the LLM into a live decision-making engine by exposing enterprise systems via LangChain Tools. SQL databases, SCADA telemetry APIs, real-time transit streams — all wrapped as secure, callable, auditable functions.

SCADA APISQL ToolsStarlink/EdgeAWSAzurePrivate VPC
Plan Your Integration
API & Database Tooling

We wrap SQL databases, SCADA telemetry APIs, and real-time transit and weather streams into secure LangChain tools — callable by the agent with full input/output logging via LangSmith, without exposing raw database credentials or API keys to the model layer.

Satellite & Edge Integration

Context windows and payloads are optimized to execute lightweight agentic queries over constrained-bandwidth networks — remote data centers, substations, and offshore platforms using Starlink or orbital satellite backhaul networks.

Secure Enterprise Deployment

All deployments occur inside the client's cloud perimeter (AWS VPC, Azure VNET, or private cloud). No data transits to third-party model providers without explicit consent; private model deployments are available for air-gapped environments.

MLOps & LangSmith Observability

The entire pipeline is instrumented using LangSmith for continuous prompt debugging, latency tracing, token cost optimization, and regression testing. Every agent run generates a complete audit trail for regulatory review.

Technical Execution Stack

What We Build With

Orchestration
LangChain LangGraph LangSmith
Vector Infrastructure
Pgvector Qdrant Pinecone
Observability & Guardrails
LangSmith TruLens Unstructured.io
Deployment
AWS / VPC Azure VNET Private Cloud

Ready to build your production AI framework?

Start with our flat-fee Infrastructure Assessment — 3 weeks, a complete data audit, and a custom LangGraph architectural blueprint your team can act on immediately.

Book an Assessment View Pricing Tiers
Investment Tiers

Structured Engagement Models for Industrial AI

Three clearly scoped tiers — from a flat-fee feasibility assessment to a dedicated engineering retainer. Each is designed as a logical progression with no hidden scope creep.

Tier 01 — Exploratory
Infrastructure Assessment
For enterprises evaluating AI feasibility, identifying high-ROI use cases, and mapping legacy data structures before writing code.
$15,000
Flat Fee / 3-Week Engagement
  • Data Architecture Audit — full review of unstructured data (PDF manuals, grid codes) and real-time telemetry pipelines (SCADA, APIs, satellite backhaul)
  • Feasibility & Token Cost Modeling — latency, context window constraints, and estimated operational costs
  • Custom LangGraph Blueprint — architecture diagram mapping multi-agent states, routing logic, and tool integration paths
  • Deliverable: AI Readiness & Architectural Roadmap ready for internal stakeholder approval or investor decks
  • $15K fee credited in full if upgraded to Implementation Tier within 30 days
Schedule an Assessment
Tier 02 — Implementation
Production-Ready MVP
For organizations ready to build, deploy, and test a fully functional, domain-specific AI agent framework within their environment.
$45,000+
Starting Price / 8–12 Week Engagement
  • Everything in the Exploratory Tier — fee credited back if upgraded within 30 days
  • Multi-Modal RAG Pipeline — layout-aware document parsing, hybrid vector/keyword search infrastructure (Pgvector/Qdrant), and metadata tagging
  • Stateful LangGraph Runtimes — deterministic multi-agent routing with built-in guardrails (Context Relevance & Groundedness validation)
  • Core Tool Integrations — connecting the LLM engine to up to 3 enterprise APIs, SQL databases, or live asset streams
  • Human-in-the-Loop (HITL) safeguards — checkpoints requiring manual sign-off before triggering critical actions
  • Deliverable: Live, sandboxed Agentic RAG Application integrated with your data, ready for user testing
Apply for Implementation
Tier 03 — Enterprise
Scale, MLOps & Retainer
For organizations looking for a dedicated AI engineering partner to continuously optimize, scale, and maintain mission-critical infrastructure models.
Custom
Monthly Retainer / Dedicated Partnership
  • Continuous Prompt Engineering & Optimization — managing regression testing and latency tuning via LangSmith
  • Edge & Satellite Optimization — tailoring context payloads for constrained bandwidth (Starlink backhaul, remote stations)
  • Custom Model Fine-Tuning — open-source LLMs tuned on proprietary domain terminology to reduce API token dependencies
  • Dedicated SLA Support — guaranteed response times for pipeline updates, security patches, and vector database scaling
  • Deliverable: Full-scale Production Deployment with continuous monitoring, audit logs, and proactive model maintenance
Contact Enterprise Engineering
The Honest Difference

Why not McKinsey, Deloitte, or Accenture?

If you've worked with large consulting firms on technology projects before, you already know the pattern. Here's how we're different — and why it matters for AI in particular.

Large consulting firms
✗Analysts write the strategy. Engineers are elsewhere.
✗6-month discovery before any code is written
✗Offshore delivery teams with 12-hour handoffs
✗AI pilots scoped to impress, not to deploy
✗Recommendations built on generic frameworks
✗You own the slides. They own the relationship.
Syntropi.io
✓Founders and senior engineers do the work
✓Blueprint delivered in 3 weeks, deployable in 12
✓Single timezone team, direct Slack access
✓Production-grade from day one — no throwaway pilots
✓Built by people who have run grid & energy operations
✓You own the system, the code, and the architecture
Tier Comparison

What's Included at Each Level

Feature Assessment
$15K		 MVP
$45K+		 Enterprise
Custom
Data Architecture Audit✓✓✓
Token Cost & Feasibility Modeling✓✓✓
Custom LangGraph Blueprint✓✓✓
Multi-Modal RAG Pipeline Build—✓✓
Stateful LangGraph Runtime—✓✓
Enterprise API Tool Integrations—Up to 3Unlimited
Human-in-the-Loop (HITL) Safeguards—✓✓
LangSmith Observability & Tracing—✓✓
Continuous Prompt Optimization——✓
Custom Model Fine-Tuning——✓
Edge / Satellite Optimization——✓
Dedicated SLA Support——✓
Assessment Fee Credit—100% credited100% credited
ROI Estimator

What is your current AI readiness gap costing you?

Adjust the inputs to reflect your operational environment. The estimator calculates the annual cost of your current state and projects the return from a Syntropi engagement.

102,000
$40$250
0.5 hrs20 hrs
1200
$1K$500K
4 hrs500 hrs
Estimated annual cost of current state
Information search overhead —
Extended MTTR cost (incidents) —
Compliance reporting overhead —
Total annual cost —
Projected Syntropi impact
Annual savings (conservative 60%) —
Assessment payback period —
First-year net return —
Estimates are illustrative and based on industry benchmarks. Actual results vary by environment and implementation scope.
Get My Infrastructure Assessment — $15,000
Common Questions

Frequently Asked

What does the $15K Assessment actually deliver? +
A comprehensive AI Readiness & Architectural Roadmap document covering: a full audit of your existing data sources and telemetry pipelines, a token cost and latency feasibility model, and a complete LangGraph architectural blueprint mapping multi-agent states, routing logic, and tool integration paths — all tailored to your specific operational environment.
Can we start with the Assessment and upgrade to the MVP? +
Yes. If you upgrade to the Implementation Tier within 30 days of receiving your Assessment deliverable, the $15,000 fee is credited in full toward the MVP engagement. This is the recommended path for organizations that need internal sign-off before committing to a larger build.
Do you work with air-gapped or highly regulated environments? +
Yes. All deployments occur within the client's cloud perimeter (AWS VPC, Azure VNET, or private cloud). For NERC CIP, FERC, or other regulatory frameworks requiring air-gapped environments, we support fully private model deployments using open-source LLMs with no data transiting to third-party providers.
What industries do you serve? +
Our primary focus is energy & utilities, grid infrastructure, logistics & supply chain, data centers, and oil & gas. These industries share the same core challenge: large volumes of fragmented, multi-modal operational data that standard AI systems cannot reliably process for mission-critical decision-making.
How does your HITL (Human-in-the-Loop) implementation work? +
We engineer StateGraph checkpoints within the LangGraph workflow that pause agent execution and surface a structured approval request before triggering any critical external action — such as drafting a regulatory deviation report, initiating a vendor dispatch, or modifying setpoint values. Human approval is required and logged before the agent proceeds. All approval decisions are stored in the audit trail.
What is the Enterprise Retainer structured around? +
Enterprise retainers are scoped based on the complexity of the production environment, the number of agent pipelines under active management, SLA requirements, and whether custom model fine-tuning is included. Pricing typically starts at $15,000/month for foundational MLOps and scales with scope. Contact us for a custom proposal.

Start with the lowest-risk entry point.

A 3-week, flat-fee Infrastructure Assessment gives you a complete LangGraph architectural blueprint — and a 100% fee credit if you move to implementation. No commitment beyond the assessment.

Book an Assessment — $15K View Technical Framework
Get In Touch

Start with an Infrastructure Assessment

We build AI systems for industries where the data is old, the stakes are high, and the margin for error is zero.

Tell us about your operational environment. We'll evaluate your data stack, identify the highest-ROI integration points, and return a complete LangGraph architectural blueprint in 3 weeks.

Response Time
Within 24 business hours
Typical Start
2–3 weeks after engagement signed
Industries Served
Energy, Grid Infrastructure, Logistics & Transit, Data Centers, Oil & Gas
Headquarters
syntropi.io

Request an Engagement

Complete all fields marked * so we can prepare a relevant technical brief before our first call.

Every submission is reviewed personally. Expect a response within 24 business hours.

Syntropi.io
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Syntropi.io
Technical Architecture

Production-Grade AI Engineering Framework

Every Syntropi engagement follows the same deterministic architecture: multi-modal RAG pipelines with auditable agent chains, zero hallucination tolerance, and real-time tool integration built on LangChain and LangGraph. Here is exactly how we build it.

Core engineering pillars
Pillar 01 / 03

Multi-Modal RAG Pipelines

We ingest complex industrial documents — schematics, SCADA logs, grid codes, asset manuals — and build vector infrastructure that answers queries accurately across decades of operational data.

  • LangChain document loaders + Unstructured.io
  • Layout-aware parsing (tables, diagrams, schematics)
  • Semantic chunking — preserves formula context
  • Hybrid retrieval: dense vector + BM25 sparse
  • Pgvector / Qdrant / Pinecone vector stores
  • LangSmith / TruLens eval + observability
Pillar 02 / 03

LangGraph Agentic Workflows

Deterministic multi-agent orchestration that replaces brittle single-prompt systems. Every agent action is auditable, every decision node is traceable, every output is verifiable.

  • LangGraph stateful agent orchestration
  • Human-in-the-loop (HITL) decision gates
  • Parallel agent execution with checkpointing
  • Structured output schemas — no free-form hallucination
  • Regression testing via LangSmith evaluators
  • Full audit trail per agent step
Pillar 03 / 03

Real-Time Tool Integration

AI agents that act — not just answer. Live connections to SCADA systems, APIs, databases, and enterprise platforms enable autonomous operational responses within defined safety boundaries.

  • SCADA / OT system connectors (Modbus, MQTT, OPC-UA)
  • REST / GraphQL API tool definitions
  • SQL + time-series database read/write
  • ERP / CMMS integration (SAP, Maximo)
  • Streaming data ingestion (Kafka, Kinesis)
  • AWS / Azure / Private VPC deployment
Technology stack
Orchestration LangChain LangGraph
Vector Store Pgvector Qdrant Pinecone
Observability LangSmith TruLens
Infrastructure AWS Azure Private VPC
Parsing Unstructured.io Camelot
Protocols MQTT OPC-UA Modbus
Streaming Kafka Kinesis WebSockets
Models GPT-4o Claude Llama 3
Phased delivery methodology
Phase 01 Weeks 1–3

Infrastructure Assessment & Data Architecture Audit — $15,000 flat fee

Full review of your unstructured data landscape, SCADA/OT integration points, and existing AI initiatives. Deliverable: a complete LangGraph architectural blueprint your engineering team can act on immediately — covering data ingestion strategy, vector store selection, agent topology, and a prioritised implementation roadmap.

Phase 02 Weeks 4–12

Production-Ready MVP — from $45,000

Build, test, and deploy a live sandboxed agentic RAG application within your environment. Includes full multi-modal pipeline, LangGraph agent orchestration, HITL decision gates, evaluation harness, and operational handoff. The $15K assessment fee credits in full against this engagement if upgraded within 30 days.

Phase 03 Ongoing

Enterprise Scale & Retainer — custom

Full production deployment across your environment, MLOps pipeline management, continuous prompt engineering and regression testing, and a dedicated Syntropi engineering partner embedded with your team. Structured as a monthly retainer with quarterly value realization reviews.

Measurable outcomes
Outcome 01

Reduced MTTR

Field technicians query decades of asset documentation in seconds instead of hours. MTTR on complex failures drops 60–80% within the first quarter of deployment.

Outcome 02

Automated Compliance Reporting

Regulatory compliance documents generated automatically from live operational data. Teams that spent 3–5 days per report complete the same task in under 2 hours.

Outcome 03

De-Risked AI Decisions

Every agent recommendation is traceable to source documents, time-stamped, and auditable. No black-box outputs. Full defensibility for regulatory review or executive reporting.

Outcome 04

Zero Production Hallucinations

Structured output schemas, retrieval grounding, and LangSmith regression testing ensure AI outputs in production are deterministic and verifiable — not probabilistic guesses.

Ready to see the architecture applied to your environment?

The $15K Infrastructure Assessment produces a complete LangGraph blueprint specific to your data stack — not a generic roadmap.

Book an Infrastructure Assessment
Syntropi.io
Pre-Engagement Preparation
Plan Your Integration — Getting Ready for Assessment
Check off what you already have in place. The more you can confirm before your discovery call, the faster we can scope your architecture and move to delivery. There are no wrong answers — gaps are exactly what the $15K Assessment is designed to map.
Readiness checklist0% complete
1Data landscape
Understanding what data sources exist and in what format
We have an inventory of our document types (PDF manuals, grid codes, engineering specs)
Helpful to know: approximate document count and age range
We know which databases store our operational data (SQL, historian, CMMS)
Examples: OSIsoft PI, Maximo, SAP PM, custom databases
We have access to at least one data source we can share in a sandboxed environment
A representative sample — no need to expose live production data initially
We can identify the 2–3 highest-value data sources causing the most operational friction
These become the first RAG pipeline targets
2Infrastructure & systems access
What systems exist and whether API or data access is feasible
We operate SCADA, DCS, or OT systems and can describe the data they generate
Protocol awareness helps: Modbus, MQTT, OPC-UA, DNP3
We have cloud infrastructure in place (AWS, Azure, GCP) or can provision a sandbox
No cloud yet is fine — we scope on-prem or hybrid architectures too
We have IT/OT contacts who can support API access or data export during the assessment
A single technical point-of-contact is sufficient
We understand our current network segmentation between IT and OT environments
Air-gapped environments are fine — we build for constrained connectivity
3Regulatory & compliance context
Compliance requirements shape architecture decisions from day one
We can name the primary compliance frameworks our AI outputs must satisfy (NERC CIP, FERC, DOT, ISO standards)
Determines audit trail requirements and output schema design
We know which reports currently require manual compilation and their approximate cycle time
These are the highest-ROI automation targets
We have a legal or compliance contact who can review AI output policies before deployment
Not needed for assessment — needed before production sign-off
4Stakeholder & budget alignment
Internal readiness often determines project velocity more than technical factors
We have executive sponsor support for an AI implementation initiative
CEO, CTO, COO, or VP Operations — someone who owns the outcome
We have identified the operational team who will use the AI system daily
End-user involvement in scoping dramatically improves adoption
Budget of $15K–$45K+ is approved or approvable for an AI assessment and MVP
The $15K assessment fee credits in full against any subsequent engagement
We have a target timeline — either a board deadline, operational event, or strategic milestone driving urgency
Helps us prioritise scope and sequence deliverables
5Success definition
Clear outcomes accelerate delivery and simplify business case approval
We can describe the specific operational problem we want AI to solve (not "do AI," but what outcome)
Example: reduce technician diagnosis time from 4 hours to 15 minutes
We know what success looks like in measurable terms (MTTR reduction, report hours saved, incidents prevented)
These become the KPIs we build and test against during delivery
We are open to a phased approach — starting with a scoped assessment before committing to full implementation
Our $15K assessment is specifically designed for this
You are well-positioned for an Infrastructure Assessment. Book your session below and we will prepare a scoped agenda based on your environment before the first call.