AI workflow automation
Turn repetitive operational tasks into reviewed, observable workflows across your current stack.
E2L AI and Automation Consulting
We help founder-led and professional teams connect their existing software stack to AI, automate repetitive workflows, and deploy custom LLM tools without turning operations into a research project.
- 01
- Integrate AI into existing tools
- 02
- Automate document-heavy workflows
- 03
- Keep sensitive data under control
01
Services
Hands-on AI integrations. Advisory judgment where it matters.
E2LC works across strategy and implementation: mapping where AI creates leverage, then building the secure automations, interfaces, and retrieval layers that fit your business.
Custom LLM tools
Build assistants, copilots, and task-specific tools that reflect your data, rules, and approval paths.
Chat interfaces
Design internal chat experiences for teams that need answers, summaries, drafting, and triage.
Document processing / RAG
Extract, classify, search, and summarize document collections with retrieval and access controls.
Sector examples. Drafted around existing operating context.
These are placeholder examples for sectors where E2LC has relevant experience.
Operational intake and support workflows
Automated routing, structured summaries, internal knowledge lookup, and secure review queues.
Private document intelligence
RAG over document databases with options for local containers and limited provider exposure.
Project admin and client communication
Brief parsing, asset tagging, proposal drafts, scope tracking, and delivery coordination.
Bookings, reviews, and team workflows
Customer response drafts, scheduling automations, inventory prompts, and service dashboards.
Designed for existing businesses
Connect the stack. Use what you already run.
Most small businesses do not need a full rebuild. They need a measured layer of automation that links documents, emails, CRMs, booking tools, spreadsheets, and human approvals into one reliable flow.
Inbox
CRM
Docs
Policy + Retrieval
LLM
Draft
Alert
Report
02 Case studies
Wearable Client Signals
A private fitness coach was managing progress across WhatsApp, spreadsheets, check-ins, and wearable screenshots. We built a dashboard that brought sleep, activity, training consistency, recovery, and weekly feedback into one review flow.
- Combined check-ins, wearable data, and progress notes.
- Highlighted clients needing review or encouragement.
- Drafted coaching messages for approval.
- Reduced admin while keeping coaching judgement central.
Case study 02
Private Document Search
A boutique law firm had years of contracts, letters, policies, and precedents spread across folders. We created a private assistant for plain-English search, summaries, version comparison, and source-linked answers.
- Created a private search layer over legal documents.
- Helped find clauses, precedents, and summaries faster.
- Returned source references for lawyer review.
- Kept outputs as drafts, not final judgement.
Storage
Private document vault
Contracts · letters · policies · precedent
Access
Permission check
Matter scope and user role
Prepare
Redact + chunk
Limit sensitive exposure
Index
Retrieval layer
Clause, matter, and source matching
Model
Local / hosted LLM
Selected by data sensitivity
Review
Draft answer with source links
Lawyer remains final decision maker
Case study 03
AI Brand Workflow
A brand designer was using AI tools across strategy, copy, image generation, and motion. We structured the process into one reusable workflow from brief and positioning through prompts, copy, campaign ideas, and presentation flow.
- Built reusable prompts for strategy, copy, visuals, and motion.
- Created model-specific prompt structures.
- Connected brand thinking with asset creation.
- Made first-round concepts faster and more consistent.
01
Brief
Client inputs and constraints
02
Positioning
Audience, category, promise
03
Voice
Naming, copy, tone system
04
Visual Prompts
Model-specific image structure
05
Motion Frames
Video tests and storyboards
06
Presentation
First-round concept deck
Case study 04
Prototype To Launch
A founder had a working demo but needed the production basics: hosting, accounts, payments, emails, database, analytics, domain setup, and admin tools. We connected the launch stack without overbuilding.
- Set up frontend, backend, database, and authentication.
- Added payments, emails, analytics, and domain connection.
- Created admin tools for users and activity.
- Focused on a maintainable launch stack.
Prototype
Launch Stack
Demo to usable product
Frontend
Backend
Database
Auth
Payments
Email
Analytics
Domain
Admin
Live product
Users can sign up, pay, receive emails, and use the product reliably.
03 Method
From messy process. To deployed automation.
Audit the workflow
We identify bottlenecks, data boundaries, integrations, and where human judgment must stay in the loop.
Build a narrow system
We create a focused version that proves value before expanding scope or adding more integrations.
Control the data path
We choose hosted, local, or hybrid model setups based on sensitivity, cost, latency, and governance.
Measure and improve
We add observability, evaluation points, and feedback loops so the system can be trusted in practice.
04 Team
Technical depth. Across models, infrastructure, and deployment.
E2LC combines hands-on AI systems experience with practical infrastructure judgment, helping businesses choose what should run in the cloud, what should run locally, and how the pieces should connect.
01
Ernest L
AI systems and LLM integration
Ernest works across the model and application layer: LLM pre-training and post-training concepts, fine-tuning, retrieval-augmented generation, multimodal input design, model selection, and agent orchestration.
His focus is turning model capability into useful business systems: choosing the right model for the task, designing the retrieval and evaluation layer, and building agentic workflows that remain observable, secure, and practical for non-technical teams.
- LLM selection, fine-tuning, and adaptation strategy
- RAG, document intelligence, and multimodal workflows
- Agent orchestration and application-layer architecture
02
Daniel C
Local AI infrastructure and deployment
Daniel focuses on the deployment side of AI systems: local LLMs, open-source infrastructure, hardware planning, and the practical tradeoffs between hosted APIs, private servers, GPUs, CPUs, and hybrid deployments.
He helps teams understand when local inference is worth it, what hardware is realistic, and how to build infrastructure that supports privacy, cost control, latency targets, and reliable day-to-day use.
- Local LLM deployment and open-source AI tooling
- GPU, CPU, server, and workstation planning
- Private, hybrid, and containerized deployment paths
Security posture
AI adoption. Without unnecessary data exposure.
Sensitive workflows can use redaction, retrieval permissions, audit logs, hosted providers with strict controls, or local/containerized inference where the risk profile demands it.
05 Book a consultation
Tell us what you are trying to automate.
Share the workflow, tools, and pain points. E2LC will respond with next steps for a practical AI or automation consultation.
Good starting points
Document-heavy process · repeated manual admin · internal knowledge search · CRM or inbox automation