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AI Orchestration · Workflows
ATC

Flow

Automated Intelligence Workflows.

MCP-Powered
Data Processing Pipelines.

Continuous data processing with MCP protocol. Scrape, process, transform, and deliver fresh intelligence daily — powered by local LLMs on your GPU infrastructure. No per-token API costs. No cloud dependency.

60–70% cost reduction vs cloud API workflows. Your data pipeline runs on your electricity, not someone else’s cloud.
70%
Cost Reduction
24/7
Pipeline Uptime
0
Per-Token Fees
Automate Your Data Pipeline
Show us your manual workflow. We'll build an automated Flow pipeline and demo it on your infrastructure.
Your data stays private. We never share your information.
01 — Capabilities

Not Just ETL — Intelligence Pipelines

ATC Flow chains AI operations into automated workflows. Scrape, clean, embed, summarize, deliver — each step configurable and auditable.

MCP Data Pipelines

Connect to any data source via Model Context Protocol — databases, APIs, web scrapers, file systems, S3-compatible storage. Unified data access for all AI workflows.

Local LLM Drafting

Use on-premise LLMs (vLLM, Ollama) for content generation, summarization, extraction, and transformation. No per-token API costs. Fixed cost, unlimited usage.

Scheduled Processing

Cron-based and event-driven workflow execution. Daily data freshness. Automated report generation. Proactive intelligence delivery — not reactive queries.

Multi-Step Orchestration

Chain operations: scrape → clean → embed → summarize → deliver. Branching logic, error handling, and parallel execution built in.

Cost Optimization

60–70% cost reduction vs cloud API workflows. No per-query fees. Local GPU compute at fixed energy cost. Break even in weeks, not months.

Monitoring & Alerts

Real-time pipeline health monitoring. Failure alerts, intelligent retry logic, automatic recovery. Dashboard visibility into every running workflow.

Calculate Your API Cost Savings
Tell us your current OpenAI/Anthropic/Google API spend. We’ll show you the exact savings with ATC Flow on local LLMs.
02 — Pipeline

From Raw Data to Delivered Intelligence

Every Flow pipeline follows a 6-stage process. Each step is configurable, auditable, and retryable.

01
Ingest

MCP pulls from sources

02
Clean

Normalize + deduplicate

03
Process

Local LLM transforms

04
Embed

Vector store for RAG

05
Analyze

Extract insights + trends

06
Deliver

Push to channels

03 — Use Cases

Intelligence Pipelines Your Team Runs Manually Today

Four high-impact workflows that Flow automates end-to-end — from data collection to delivery.

Market Intelligence

Daily competitive monitoring on autopilot

  • Scrape competitor websites, news, and filings
  • Summarize changes with local LLM
  • Generate daily intelligence briefs
  • Deliver to Slack, email, or dashboard
  • Track trends and surface anomalies over time

Content Pipeline

Source → Draft → Review → Publish

  • Ingest source materials from RSS, APIs, and databases
  • Draft content with on-premise LLM
  • Route for human review with tracked approvals
  • Publish to CMS or distribution channels
  • Track performance metrics post-publish

Data Enrichment

Transform raw records into structured intelligence

  • Pull records from CRM, ERP, or legacy databases
  • Enrich with external data sources via MCP
  • Classify and tag with AI-powered categorization
  • Write enriched data back to source systems
  • Trigger downstream workflows on completion

Automated Reporting

Scheduled intelligence reports without manual work

  • Pull metrics from databases on daily/weekly cadence
  • LLM generates narrative analysis from raw numbers
  • Format into PDF reports or live dashboards
  • Distribute to stakeholders via email or Slack
  • Flag anomalies and deviations automatically
04 — Compare

Cloud API Workflows vs ATC Flow

LangChain + OpenAI APIs, custom scripts hitting cloud endpoints — they work until your bill arrives.

Cloud API Workflows (LangChain, OpenAI, etc.)

  • Per-token pricing that scales with volume
  • Data sent to external cloud servers for processing
  • Rate limits throttle high-volume pipelines
  • Vendor model changes break existing pipelines
  • No built-in orchestration or scheduling
  • Custom code required per integration
  • Compliance risk with data leaving premises

ATC Flow On-Premise

  • Fixed GPU cost — unlimited LLM processing
  • All data stays on your infrastructure
  • No rate limits — your hardware, your throughput
  • You control which models run and when they update
  • Built-in scheduler, orchestrator, and monitor
  • MCP unified connector for all data sources
  • DPDP-ready — data never leaves premises
Stop Paying Per Token
Deploy ATC Flow once — unlimited LLM processing, MCP-connected data sources, scheduled delivery.
Your Data. Your Premises. Your AI.
05 — Industries

Data Pipelines for Every Sector

Where data freshness and sovereignty both matter, ATC Flow delivers daily intelligence without cloud exposure.

Healthcare

Clinical data processing + research pipelines

  • Research paper ingestion and summarization pipelines
  • Patient data enrichment from multiple EHR systems
  • Drug interaction monitoring from regulatory feeds
  • HIPAA-ready on-premise processing end-to-end

BFSI

Market data + compliance pipelines

  • Daily market data aggregation and trend analysis
  • Regulatory change monitoring across jurisdictions
  • Credit risk enrichment from multiple data providers
  • Audit-ready pipeline logs for compliance reporting

Manufacturing

Supply chain + quality intelligence

  • Supplier data enrichment from procurement systems
  • Quality metrics aggregation across production lines
  • Predictive maintenance reports from IoT sensor data
  • Bill of materials analysis with cost optimization

Education

Content generation + learning analytics

  • Student performance data aggregation across systems
  • Research output tracking and citation analysis
  • Curriculum content generation from source materials
  • Accreditation report automation from institutional data

Government

Sovereign data pipelines for public sector

  • Citizen feedback analysis from multiple channels
  • Policy impact monitoring from public data sources
  • Inter-department data harmonization pipelines
  • Air-gapped deployment for classified data processing

Media & Publishing

Content intelligence at editorial speed

  • News aggregation and summarization from global feeds
  • Content drafting pipelines with editorial workflows
  • Audience analytics enrichment from multiple platforms
  • Automated newsletter generation on daily schedule
06 — Technology Stack

Built for Local GPU Performance

Every component runs on your hardware. MCP protocol connects everything. Docker-deployed for portability.

MCP ProtocolvLLM/OllamaCron SchedulerWeb ScraperpgvectorDockerOn-Premise

Automate Your Intelligence Pipeline

Show us your manual data workflow. We’ll build an automated Flow pipeline and demo it live — on your infrastructure, with your data.

Your Data. Your Premises. Your AI.

FAQ

Frequently Asked Questions

How is ATC Flow different from Zapier or Make?

Zapier and Make trigger pre-built integrations on cloud APIs; ATC Flow runs entire data pipelines on your infrastructure, including the LLM step. No per-action pricing, no cloud egress, custom MCP tools.

How is ATC Flow different from your AI Workflow Automation service?

ATC Flow is the productised data-pipeline platform — installable in days, scoped to MCP-orchestrated processing jobs. The Workflow Automation service is a custom engagement where we design end-to-end agent-based workflows for your specific approvals, escalations, and audit needs. Most customers buy both.

Where does the 60–70% cost reduction come from?

Replacing per-token cloud LLM API calls with local LLM inference on amortised GPU hardware. For workflows above ~5M tokens per month, on-prem inference breaks even within 6 to 9 months.

What kinds of pipelines do customers build?

News and competitor monitoring, financial filings extraction, supplier price scraping, document intake and classification, internal report generation, and data-quality checks across multiple databases.

How are jobs scheduled?

Cron, event-based, or webhook-triggered. Job graphs support fan-out, fan-in, retries with exponential backoff, and dead-letter queues. Observability via Prometheus and Grafana dashboards.

Does ATC Flow handle structured outputs reliably?

Yes. JSON-mode generation with schema validation, automatic retries on parse failure, and grammar-constrained decoding for strict-format outputs (XML, SQL, regex-bounded tokens).

Can pipelines call external APIs?

Yes — outbound is allowed for explicitly whitelisted endpoints, audited per call. Most customers route external calls through a single network DMZ for centralized auditing and rate-limiting.

What is the operational footprint?

A single H100 GPU plus 8 vCPU control plane handles ~10,000 LLM-step jobs per day with redundancy. Storage scales with raw data and intermediate outputs.