DARKKEVNLABS

Practical AI development

Tell me the work that keeps getting repeated.

I turn that workflow into a useful agent, app, or automation. Sometimes the answer is OpenAI. Sometimes it is Claude or Gemini. Sometimes it is not AI at all.

Show me the workflow How I work
DESIGN → BUILD → SHIPExplore this page

AI agents & connected offices

Put your knowledge to work.

Your team knows the business. Your tools hold the information. I help connect them through useful agents, automations, and internal applications.

AI agent development

Assistants that qualify enquiries, prepare drafts, find information, or move a task forward. Built around your workflow, with clear tools, permissions, and human approval where it matters.

Agentic layers

Add an action layer to the software you already use. An agent can interpret a request, gather context, coordinate tools, and propose the next step across a connected workflow.

Office integration

Connect email, calendars, documents, spreadsheets, CRM records, and internal apps. Microsoft 365, Google Workspace, and other integrations are scoped around your existing access and available APIs.

Company knowledge

Turn approved policies, manuals, project notes, and procedures into searchable context. Give teams answers with source references, role-based access, and a way to flag missing or outdated information.

Let’s connect your workflow

AI platforms

Choose the platform after the problem is clear.

Model choice is based on the job, data, privacy, quality, speed, tool support, deployment needs, and operating cost.

OpenAI

OpenAI, ChatGPT, and Codex

OpenAI API integrations for text, vision, voice, structured outputs, tool use, and agent workflows, with Codex available as part of the development and review process.

  • OpenAI API
  • ChatGPT integrations
  • OpenAI Codex
  • AI agents

Anthropic

Anthropic Claude and Claude Code

Claude API integrations for long-context work, document analysis, assistants, and tool-connected systems, plus Claude Code workflows for software development.

  • Claude API
  • Claude Code
  • MCP tools
  • Document workflows

Google AI

Google Gemini and Vertex AI

Gemini API and Vertex AI paths for multimodal products, grounded experiences, function calling, search, data workflows, and Google Cloud deployments.

  • Gemini API
  • Vertex AI
  • Multimodal AI
  • Function calling

Microsoft

Azure OpenAI and Copilot

Azure OpenAI architectures, Microsoft Copilot Studio, GitHub Copilot workflows, identity-aware deployments, and Microsoft ecosystem connections.

  • Azure OpenAI
  • Copilot Studio
  • GitHub Copilot
  • Microsoft Entra ID

Model options

Grok, Llama, Mistral, and Perplexity

Additional hosted and open-model options can be compared for grounded search, private infrastructure, speed, model routing, cost, and deployment control.

  • xAI Grok
  • Meta Llama
  • Mistral AI
  • Perplexity

Architecture

RAG, MCP, tools, and evaluation

Retrieval-augmented generation, Model Context Protocol servers, business tool connections, human approvals, observability, and evaluations make the model part of a dependable system.

  • RAG development
  • MCP servers
  • Tool use
  • AI evaluation

What gets built

The model is one layer. The product is the whole system.

Useful AI needs an interface, approved context, tools, permissions, tests, monitoring, and a clear human handoff.

How a dependable AI system fits together. Packets show the path a request takes. Hover a node to see what it connects to.

AI agents and assistants

Goal-driven agents, customer or staff assistants, intake systems, research workflows, and task automation with controlled tool access.

Knowledge and RAG search

Search and answer from approved documents, websites, product data, policies, records, or internal knowledge with traceable source context.

Multimodal AI

Workflows that can understand or generate combinations of text, images, audio, voice, video, forms, and structured business data.

MCP and business tools

Model Context Protocol connections and custom tool interfaces for CRMs, databases, calendars, email, ecommerce, support, and internal systems.

AI app development

Customer-facing products, internal tools, dashboards, portals, mobile experiences, and existing applications with focused AI capabilities.

Safety and evaluation

Permissions, privacy boundaries, prompt-injection controls, output checks, test cases, monitoring, cost controls, and human approval where it matters.

Independent guidance

I am not loyal to a model. I am loyal to the job.

Some products need a frontier hosted model. Others need fast structured extraction, grounded search, a private model, several routed models, or no generative AI at all. I compare the options before tying the product to one provider.

Production decisions include privacy, data retention, permissions, latency, context size, multimodal needs, reliability, evaluation results, provider portability, and the ongoing cost of each successful task.

AI development FAQ

Questions before an AI build starts.

What kinds of AI systems do you build?

Custom AI agents, assistants, workflow automations, retrieval systems, lead qualification tools, internal knowledge search, AI-enabled websites, and AI features for web or mobile apps.

Can you build with the OpenAI API, ChatGPT, or OpenAI Codex?

Yes. A project can use OpenAI APIs for text, vision, voice, structured outputs, tool use, or agent workflows. OpenAI Codex can also support the development and review process when it fits the project.

Do you work with Anthropic Claude and Claude Code?

Yes. Anthropic Claude can support production assistants, analysis, tool-connected workflows, and long-context use cases. Claude Code can be part of an AI-assisted software development workflow.

Can you compare OpenAI, Claude, Gemini, Grok, Llama, and Mistral for a project?

Yes. Model selection should consider output quality, privacy, latency, context, tool support, deployment options, and operating cost. The recommendation is based on the product requirement rather than loyalty to one provider.

What are RAG and MCP?

Retrieval-augmented generation, or RAG, lets an AI system answer from approved documents or data. Model Context Protocol, or MCP, gives compatible AI clients a structured way to connect with tools and business systems.

Can AI be added to an existing website or application?

Yes. AI can be introduced as a focused feature such as search, intake, summarization, document processing, customer support, content operations, or an internal assistant without rebuilding the entire product.

DARKKEVN Labs is an independent implementation studio. OpenAI, ChatGPT, Codex, Anthropic, Claude, Claude Code, Gemini, Google, Microsoft, GitHub Copilot, xAI, Grok, Meta, Llama, Mistral, and Perplexity are trademarks of their respective owners. Their mention describes compatible technology and does not imply endorsement or partnership.

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Got a messy workflow? Send it to me.

Share the workflow, users, data, privacy needs, current tools, and the result the AI system should produce.

Delivered to kevin@kevinjohnston.ca.