Build AI Copilots That Actually Help with Copilot Development
We build AI copilots that sit inside your team's workflow — not beside it. Context-aware assistants that draft, suggest, and automate while keeping humans in control of every decision that matters.
Trusted by innovative teams worldwide
Certified to Build Production Copilots
Our engineers bring deep expertise in the LLM orchestration and UX patterns that make copilots work.
Complete Copilot Engineering
From context ingestion to inline suggestions — every layer of your AI copilot, built for real workflows.
Context-Aware Intelligence
Copilots that understand your codebase, CRM data, ticket history, or document context in real time. We build context pipelines that feed the right information to the LLM at exactly the right moment.
Tool Calling & Action Execution
Your copilot doesn't just suggest — it acts. We wire LLMs to your internal APIs, databases, and tools so the copilot can draft emails, create tickets, update records, and trigger workflows on behalf of users.
Human-in-the-Loop Workflows
Accept, reject, or edit every copilot suggestion. We design approval flows, confidence thresholds, and escalation paths that keep humans in control while AI handles the heavy lifting.
Copilot UX & Inline Experience
Copilots fail when the UX is wrong. We design inline suggestions, side panels, slash commands, and ghost text patterns that feel native to your product — not bolted on.
LLM Orchestration & Routing
Multi-model architectures that route tasks to the right LLM. Simple autocomplete goes to a fast, cheap model. Complex reasoning goes to GPT-4o or Claude. All invisible to the user.
Learning & Personalization
Copilots that get smarter over time. We build feedback loops, user preference models, and team-level learning that improve suggestion quality with every interaction.
Your Team Deserves an AI Copilot That Gets the Job Done.
Book a free copilot workshop — we'll identify your highest-impact use case and prototype it in two weeks.
Copilots aren't chatbots. They're workflow multipliers.
The best copilots disappear into the workflow. They draft the email, suggest the code, surface the data — and let the human make the final call. That's what we build.
Copilots That People Actually Use
Most AI copilots get abandoned within a month. Ours don't — because we obsess over context quality, suggestion relevance, and the UX details that make adoption stick.
Why Teams Build Copilots with OpenMalo
We've built copilots for developers, sales teams, support agents, and analysts — we know what makes adoption stick.
Tell Us About Your Copilot Vision
Describe the workflow you want to augment and we'll respond with a copilot concept and prototype plan within 24 hours.
Our Engagement Process
Workflow Analysis
We shadow your team, map their workflows, identify repetitive tasks, and pinpoint where AI suggestions would save the most time without disrupting focus.
Context & LLM Architecture
Design the context pipeline, select LLMs for each task tier, define tool calling schemas, and architect the human-in-the-loop approval flows.
Copilot UX & Prototype
Build the inline experience — suggestion UI, acceptance gestures, feedback mechanisms — and ship a working prototype connected to real context in three weeks.
Measure & Iterate
Track adoption metrics, suggestion quality, and user feedback. Tune prompts, adjust context windows, and refine the UX based on real usage data.
Scale & Personalize
Roll out to the full team with per-user personalization, team-level learning, and continuous improvement loops that make the copilot smarter every week.
What Our Clients Say
“Our sales team was spending 90 minutes per prospect on research and email drafting. OpenMalo built us a sales copilot that cuts that to 20 minutes — and the emails convert better because they're grounded in real prospect data.
“We tried building our own code copilot with raw GPT-4. The suggestions were generic and developers ignored them. OpenMalo's version understands our codebase, our conventions, and our APIs. Acceptance rate went from 12% to 67%.
“The human-in-the-loop design was the key. Our support agents trust the copilot because they can see exactly why it's suggesting each response, edit it, and hit send. Ticket resolution time dropped 55% in the first month.
67% Suggestion Acceptance Rate — 4x Higher Than Their DIY Attempt
Code Copilot for CodeVault's Internal Platform
How we built a context-aware code copilot that understands CodeVault's proprietary frameworks, API conventions, and coding standards — achieving 67% suggestion acceptance vs. 12% with their previous generic GPT wrapper.
Generic AI suggestions developers ignored
CodeVault's engineering team built a code assistant using GPT-4, but it produced generic suggestions that didn't follow their internal frameworks, API patterns, or coding standards. Developers stopped using it within two weeks.
Our Approach: RAG over the entire codebase with repo-aware chunking, custom embedding model trained on their code patterns, function calling for API lookups, IDE extension with inline ghost text UX, and per-developer preference learning — shipped in 5 weeks.
Read Full Case StudyFrequently Asked Questions
A chatbot sits in a chat window and waits for questions. A copilot is embedded directly in your workflow — it proactively suggests, drafts, and acts within the tools you already use. Think GitHub Copilot inside your IDE vs. ChatGPT in a browser tab.
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