Retrieval over your own documents
RAG systems that put your internal documents behind the workflow, so the model works from material your team already uses.
RAGPostgresAthena
ai@techdriven:~$ plan --scope chicago
AI infrastructure consulting · Chicago
AI infrastructure consulting for Chicago businesses.
We design and build the systems behind practical AI: retrieval over your documents, AWS Bedrock pipelines, private models, and MCP integrations that connect AI to the tools you already run. Then we document the stack so your team can operate it.
Chicago-basedCloud or self-hostedNo vendor lock-in
$ ai-stack plan --target production
dataprivate documents
retrievalRAG
inferenceBedrock | Ollama
toolsMCP | Slack | ticketing
automationn8n | Activepieces
operationslogs | dashboards | runbooks
statusready for review
ai@techdriven:~$ ls operating-layer/
What we build
The infrastructure behind useful AI.
A useful AI system has to reach the right data, connect to the right tools, and leave enough evidence for an engineer to operate it. The model is one layer. We build the operating layer around it.
Cloud AI on AWS
Bedrock pipelines when cloud is the right boundary, connected to production infrastructure built and reviewed as code.
BedrockAWSTerraform
Private model deployments
Ollama and self-hosted inference for environments where the data cannot leave the building.
OllamaLinuxSelf-hosted
AI inside real workflows
MCP, Slack, and ticketing integrations that connect models to the systems people already use — plus AI steps inside self-hosted n8n and Activepieces automations. Not a chatbot bolted on top.
MCPn8nActivepieces
ai@techdriven:~$ resolve --data-boundary
Architecture follows the constraint
Cloud, private, or both.
Bedrock where the cloud is fine. Local models where the answer is no. We map the data path and operating tradeoffs before choosing the runtime, so the architecture fits the environment instead of forcing the environment to fit the model.
See our full consulting practice| Layer | Runtime | Operating result |
|---|---|---|
| cloud | AWS Bedrock | production infrastructure as code |
| private | Ollama | on-prem inference for sensitive data |
| integration | MCP + APIs | Slack, ticketing, internal tools |
| automation | n8n + Activepieces | AI-assisted workflows on triggers and schedules |
| operations | Grafana + CloudWatch | monitoring, dashboards, runbooks |
ai@techdriven:~$ cat field-notes/ai-in-the-ticket-queue.md
Field-tested before client-facing
We test AI on our own operations first.
We ran AI through our own helpdesk for six months before we let it near a client queue. We kept triage, response drafting, and root-cause write-ups. We killed autonomous resolution and chatbot-as-tier-one. Humans close tickets.
Read the full field note6 monthsrunning AI through our own helpdesk first
~94%triage-classification accuracy on our queue
41 → 9 minmedian first-response time with draft-first replies
ai@techdriven:~$ cat engagement.md
Fixed scope per phase
How an AI infrastructure engagement runs.
// stop after any phase. keep everything we produced.
01
Audit
Two weeks, read-only. We map the current workflow, data sources, infrastructure, cost, and risk. You keep the findings whether or not you hire us for the next phase.
Access reviewData pathRisk register
02
Design
The target architecture on paper before anything moves — cloud or private, retrieval and integrations, tradeoffs and rollback written down.
ArchitectureModel boundaryRollback plan
03
Build
We build the pipelines and integrations, with infrastructure as code where it belongs. Your engineers sit in the reviews, so nothing lands as a black box.
RAGMCPInfra as code
04
Handover
Runbooks, dashboards, and a walkthrough with the people who will own it. If you never need to call us again, that counts as the job going well.
RunbooksDashboardsTeam walkthrough
ai@techdriven:~$ whoami --region chicago
Local when the work needs a room
Chicago-based. Onsite across Chicagoland.
Architecture, integrations, and day-two operations can run remotely. When discovery needs the actual network, security boundary, rack, or people in the room, we work onsite across Chicagoland.
The engineer who scopes the work is the engineer who builds it. No sales-layer translation, no mystery handoff.
ai@techdriven:~$ cat faq.md
Plain answers
AI infrastructure consulting in Chicago, plainly answered.
- What does AI infrastructure consulting include?
- We audit the workflow and data path, design the target architecture, build retrieval and integrations, and hand over runbooks and dashboards your team can operate.
- Can our data stay on premises?
- Yes. We deploy self-hosted models with Ollama and on-prem inference where the data cannot leave. Where cloud is acceptable, we also build with AWS Bedrock.
- Do you build RAG systems?
- Yes. We build retrieval over your own documents and connect it to the workflow around it, rather than treating the model as a standalone demo.
- Do you build AI-assisted automation workflows?
- Yes. We build automations on self-hosted n8n or Activepieces, with AI steps where they earn their place. For code-heavy pipelines we reach for Windmill, Kestra for data orchestration, or Node-RED for event wiring — all self-hosted, all yours. Read the five-tool field note
- Do you work onsite in Chicago?
- TechDriven is based in Chicago, with onsite work across Chicagoland and remote support everywhere else.
- Will we be locked into TechDriven?
- No. Admin access, repositories, configurations, and runbooks stay in your tenancy and your name from day one.
- How does an engagement start?
- Our consulting process starts with a two-week, read-only audit. You keep the findings, and later phases are scoped separately.
Start with the real constraint
Bring us the workflow, the data boundary, and the ugly parts.
An engineer will map the system behind the model — what it connects to, where it runs, how it is observed, and how your team takes it over.