AI workflow automation
Turn repetitive operational tasks into reviewed, observable workflows across your current stack.
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 and agent tools
Build assistants, copilots, agentic workflows, and task-specific tools with the right 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.
Method. From messy process to organised automation.
We start narrow, keep data boundaries explicit, and build systems that can be observed and improved after launch.
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 with the right agent orchestration, agent skills, and integrations before expanding scope.
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.
02 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
03 CASES
Fitness Client Management
An online fitness coach had roughly 200 clients to monitor each week, with updates arriving across text, WhatsApp, email, Instagram DMs, images, videos, and wearable screenshots. We built an Openclaw/Hermes agent to triage communications, summarise them to the coach's specification, and combine them with wearable API data in one client dashboard.
- Processed multimodal client updates from messaging, email, images, and video.
- Summarised incoming communications to the coach's preferred review format.
- Connected wearable APIs and screenshots into a unified progress dashboard.
- Gave the coach one place to review client progress and follow-up needs.
Boutique Legal AI Solution
A boutique law firm wanted to use AI in legal work, but had valid concerns around data security, confidentiality, and the cost of enterprise legal AI platforms. We helped them compare vendors including Legora and Harvey against building their own, then landed on a hybrid Local LLM and cloud setup supported by local hardware where sensitivity mattered.
- Assessed Legora, Harvey, seat minimums, security posture, and likely cost.
- Designed a hybrid architecture with Local LLMs on local hardware for sensitive work.
- Created private search, summaries, version comparison, and source-linked answers.
- Kept AI outputs as lawyer-reviewed drafts rather than final legal judgement.
AI Brand Design Workflow
A brand designer was losing time and budget across Midjourney, Nano Banana Pro, and AI features in Adobe, Canva, Figma, and other design tools. We created a streamlined, model-agnostic application where documents, inspiration, existing designs, and draft assets could be uploaded, visually understood by an LLM, and kept in context for generating images, videos, mockups, and brand documents.
- Organised multimodal inputs including documents, images, designs, and inspiration.
- Built reusable prompt structures for strategy, copy, image, video, and mockup work.
- Kept source material and prompt libraries in context for generation.
- Made the workflow model-agnostic so newer generation tools can be swapped in.
- Helped the designer turn around more client work each week.
Prototype AI App Launch
A founder with a strong public-sector problem insight wanted help turning a software prototype into an AI product. He had good software fundamentals, but needed support with LLM integration, prompt engineering, and agent orchestration for browser-use agents that could run in the background for users.
- Designed the LLM integration strategy around the product workflow.
- Built prompt structures and agent skills for repeatable user tasks.
- Architected background browser-use agents for process automation.
- Helped move the prototype toward a usable AI application.
04 CONTACT
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.