Software School Contact us ↗
EN | TR

Enterprise AI training partner

Turn AI adoption into a lasting skill your whole organization owns

From executives to engineering teams, we design AI and technology training built around your company — helping people use AI tools safely, effectively, and in ways that actually fit how your business works.

Book a free discovery call → Explore our programs

Looking for individual software training instead? Explore Coyotiv Software School →

Vendor-agnostic — we teach acrossChatGPT · Claude · Gemini · Copilot · Cursor · Open-source models

How we work

How we prepare your organization for the AI era

01

Assessment

We assess how ready your organization is for AI — current skills, tools in use, data and security needs, the obstacles your teams are running into, and where AI can create the most value.

Run an AI readiness assessment →
02

Training

We teach your teams not just how to use AI tools, but how the technology works, what problems it can solve, and how it fits into real business processes. Programs are led by specialists across roles, building a shared language and capability across the organization.

Explore corporate programs →
03

Strategy & Roadmap

We build an actionable roadmap that ties your AI investments to concrete business goals — identifying priority use cases, sequencing projects by value and feasibility, and setting clear owners and a realistic timeline for each stage.

Build a strategy & roadmap →
04

Implementation & Transformation

We don't leave plans in slide decks. Working alongside Coyotiv's software and AI teams, we build pilot projects, stand up data infrastructure, integrate AI into your existing systems, and get your people using these tools in daily work — from idea to a working product, if that's what you need.

Bring your AI project to life →

Not sure where to start?

Set up a 30-minute intro call with a senior consultant.

Book a discovery call →

The problem

The real challenge: turning AI tools into enterprise value

The data across markets tells a consistent story: AI is everywhere, but converting it into measurable business value is still rare.

Adoption is accelerating — but stays wildly uneven by company size

Across OECD countries, the share of firms using AI reached 20.2% in 2025, up from 14.2% the year before and just 8.7% two years earlier. But the gap by size is stark: roughly 52% of large firms (250+ employees) use AI, compared with about 17% of small firms.

Even in the most advanced economies, adoption among large firms tops out around 60–70% — meaning a large share of even the biggest companies have not yet captured this opportunity in any systematic way.

Source: OECD, 2025

The biggest barrier isn't technology — it's expertise

For companies that haven't adopted AI, the top obstacle is a lack of in-house expertise — followed by not knowing how to use it, low awareness of the technology, and security concerns.

In other words, what's slowing companies down isn't missing tools; it's missing knowledge, capability, and a workable roadmap.

Usage is common; scaling to real value is rare

88% of organizations now report regular AI use in at least one business function, up from 78% a year earlier. Yet only around a third have moved past piloting to scale AI across the enterprise — and only about 6% qualify as "high performers" capturing more than 5% of EBIT from AI.

Using AI and generating value from AI are not the same thing.

Source: McKinsey, State of AI, 2025

The companies capturing the most value are redesigning how work gets done

One trait consistently separates the highest-value AI adopters from the rest: instead of just speeding up existing workflows, they rebuild them. High performers are roughly three times more likely to fundamentally redesign their processes.

AI transformation isn't a new tool bolted onto old work. The real value shows up when you redesign how the work gets done.

Source: McKinsey, State of AI, 2025

Strategy comes before tools

In a BCG study of roughly 12,000 employees, managers and leaders published in 2026, 74% of frontline employees said they use AI regularly, and 42% of regular users reported saving at least eight hours a week. Yet most organizations still haven't worked out how to turn that saved time into measurable business value.

The study's central finding: a clear AI strategy matters even more for long-term success than access to tools. Without clear goals, redesigned processes and a real corporate strategy, time saved doesn't translate into business results.

Source: BCG, 2026

The training gap persists

Only 36% of employees say they're satisfied with the AI training they've received, according to BCG's "AI at Work" research. When employees can't access the right tools, more than half say they'll turn to unsanctioned alternatives instead.

Without shared rules for what information can be shared with which tool, that shadow usage quietly increases security and data risk.

Source: BCG, "AI at Work," 2025

Employer priorities are shifting fast

In a World Economic Forum survey of over 1,000 employers across 55 economies — representing more than 14 million workers — 86% expect AI and information-processing technologies to transform their business by 2030, and AI and big-data skills top the list of fastest-growing competencies. Separately, PwC research finds that revenue growth per employee in the most AI-exposed sectors runs roughly three times higher than in the least exposed ones, with productivity gains accelerating sharply since generative AI went mainstream — driving skill requirements in the most exposed roles to change about 66% faster than elsewhere.

Sample programs

Training programs designed around your organization

AI for executives

AI strategy, investment priorities, risk, organizational transformation, and decision-making.

AI literacy for every employee

The fundamentals of generative AI, safe usage, effective prompting, and everyday work scenarios.

Department-specific AI training

Tailored programs for marketing, HR, sales, finance, legal, operations, and customer service.

AI for software teams

Cursor, GitHub Copilot, Claude Code, and AI-assisted software development workflows.

AI product development

Hands-on product building — from idea to prototype, agent systems to RAG architectures.

AI governance & safe usage

Data security, internal policy, responsible AI use, and human oversight.

Not sure which program is right for you?

Set up a 30-minute intro call with a senior consultant to design training that matches your needs.

Book a discovery call →

Format

Let's find the right format together

Executive briefing

A 60–90 minute session for senior leadership.

Half or full-day workshop

Hands-on work focused on a specific team and use case.

Training program

A multi-week development program with assignments and follow-up.

Corporate academy

A comprehensive learning path across departments and seniority levels.

Consulting & implementation

Designing and building pilot AI projects after training.

What sets us apart

Why Coyotiv

Learn from people building the technology

Programs are taught by specialists who build real software products, AI systems, and engineering teams — not full-time trainers reciting slides.

Built for your company, not a template

Content is adapted to your industry, teams, tools, and use cases rather than a one-size-fits-all deck.

Both a business and a technical lens

Strategy for leadership, hands-on practice for employees, technical depth for engineers.

You keep the usable output

Every program ends with playbooks, workflows, and pilot-project recommendations your team can put to work right away.

Tool-agnostic by design

We help you choose between ChatGPT, Claude, Gemini, Copilot, or open-source models based on what your business actually needs.

Support doesn't end at the last session

Follow-up sessions, mentoring, and applied support help teams bring what they learned into real daily workflows.

Solutions

Case Studies

Intelligent Company Search

Tag: Company discovery & market intelligence
Sector: Used across venture capital, investment, market research, B2B sales, and strategic partnerships.

The problem

Existing company databases handed users millions of records and complex filters instead of answers. An investor, salesperson, or business-development lead had to sift through results manually — cross-checking company sites, LinkedIn profiles, funding history, and news one by one.

The process turned into research projects lasting days, produced static reports that went stale almost immediately, reduced real intent down to rigid filters (excluding good-fit companies in the process), and returned scores no one could defend or explain. What the client needed wasn't more records — it was reliable, explainable company recommendations they could justify in a decision meeting.

What we built

Coyotiv built an agentic company-search platform where users ask questions in plain language and get a reasoned result for every company. Instead of translating a query into rigid, brittle filters, the system analyzes it semantically — first measuring the real distribution of industries, regions, and company sizes in the dataset so every query is calibrated against the actual data. For each company, it produces not just a score but a summary, strengths, risks, and an explainable verdict, streaming results as they're ready and treating follow-up questions as a continuation of the same research thread.

Prompts were managed through Raison and tested like software code. Langfuse tracked agent behavior and production outputs.

Who it's for

  • Venture capital and investment teams
  • Deal-sourcing and market-research teams
  • B2B sales and business-development professionals
  • Strategic partnership teams
  • Companies researching suppliers or partners
  • Leaders evaluating new market opportunities

The outcome

The platform turned company discovery from a filter-driven data scan into an answer-first, conversational research process. Users built defensible shortlists in minutes instead of days, got explainable assessments they could bring straight into a decision meeting instead of opaque scores, and rolled out new use cases on the same infrastructure without building a separate product.

Technologies used: Embedding-based semantic search, MongoDB Atlas Vector Search, OpenAI SDK, Vercel AI SDK, Raison Prompt Management, Langfuse Agent Tracing.

Network Intelligence

Tag: Population & talent intelligence
Sector: Built for public institutions, economic-development agencies, workforce-planning teams, and organizations analyzing talent migration.

The problem

Professional profile data existed in huge volume, but wasn't usable for strategic decisions. Ministers, program managers, and senior decision-makers couldn't get a direct answer when they wanted to know which skills were concentrated in a city, who was arriving, who was leaving, where graduates were heading, or which institutions were feeding which sectors.

Existing systems showed individual profiles one at a time, couldn't classify free-text job titles, skills, and sectors into anything comparable, and made every strategic question dependent on a data team's backlog. The client didn't need more profile data — it needed a current, auditable map of the talent ecosystem that decision-makers could query directly.

What we built

Coyotiv built a conversational population-intelligence platform that unifies scattered professional profile data under a shared classification system, letting non-technical users query it in plain language. The system maps free-text titles, skills, and sector tags from hundreds of thousands of profiles onto a common taxonomy, accepts questions by text or voice, and answers with live, auto-generated charts. It draws on a knowledge base of roughly 250 pre-defined answers, curated aggregations, and a live data cohort — and when no ready answer exists, it generates new analysis on the fly within a constrained, safe query environment.

Voice interaction was powered by the OpenAI Realtime API and Google Gemini Live.

Who it's for

  • Ministries and public institutions
  • Economic-development agencies
  • Municipal and city governments
  • Workforce and talent-planning teams
  • Organizations studying migration and talent mobility
  • Universities and education-policy leaders
  • Program owners and public administrators
  • Senior decision-makers
  • Policy and strategy teams without a data analyst

The outcome

The platform turned scattered individual professional records into a unified, plain-language-queryable map of the talent ecosystem. Users asked strategic questions directly instead of waiting in a data team's queue, generated charts and tables in seconds by text or voice, and moved from static annual reports to a continuously updated live analysis environment.

Technologies used: Taxonomy-constrained classification, OpenAI Agents SDK, OpenAI Realtime API, Google Gemini Live.

Intelligent Talent Search

Tag: Talent search & professional networking
Sector: Used across recruiting, talent discovery, mentorship, community matching, and professional networking.

The problem

Traditional recruiting worked largely as a passive process. Employers posted a role, waited for applications, and often never reached the best-fit candidates at all — especially professionals who weren't actively job-hunting but would have been a strong match, and who fell outside keyword-based search entirely.

Existing systems forced complex hiring needs down into a handful of keywords, missed strong candidates who described the same skills in different words, and turned the first stage of hiring into weeks of manual screening. The client didn't just need a better candidate search engine — it needed a system that understood the hiring need, found the candidates, and made a first-pass assessment before any human review began.

What we built

Coyotiv built a hybrid talent-search system that analyzes a short, plain-language search request — or a full job posting — to surface the strongest matches across the network. The system splits the request into semantic and non-semantic components, applying hard requirements like required experience, location, or seniority through filters and keyword search, while evaluating the meaning, context, and intent behind the role through semantic vector search. The two result sets are merged into a single ranking using Reciprocal Rank Fusion.

The search process was run by a multi-agent structure designed to mirror how an expert recruiter evaluates candidates. Agent behavior was managed through Raison, with BRAID prompt compilation delivering faster, higher-quality, lower-cost results from smaller models.

Who it's for

  • HR and talent-acquisition teams
  • In-house and independent recruiters
  • Companies hiring for leadership or technical roles
  • Mentorship-program managers
  • Professional community and membership platforms
  • Workforce-development organizations
  • Professional matching and networking teams
  • HR-tech companies screening candidates at scale

The outcome

The platform turned talent discovery from a passive post-and-wait process into an active search that understands the hiring need and reaches the right people directly. Users generated a shortlist in minutes by feeding in a full job posting, surfaced strong matches that classic keyword search would have missed, and meaningfully cut manual first-round screening time. System quality was continuously measured through deterministic tests and LLM-as-judge evaluations.

Technologies used: Multi-agent orchestration, Raison Prompt Management, BRAID prompt compilation, multi-embedding infrastructure across OpenAI/Gemini/Voyage, hybrid vector and keyword retrieval, Reciprocal Rank Fusion, MongoDB Atlas Vector Search, Langfuse Agent Tracing, deterministic and LLM-as-judge evals, OpenAI SDK, Vercel AI SDK, Google Vertex AI.

Our team

Trainers

Armağan Amcalar

Founder and CEO of Coyotiv, and CTO of OpenServ Labs, Armağan is a technology leader, software architect, and trainer with more than 20 years of software development experience. Over his career he has helped hundreds of engineers grow through team leadership, engineering management, and CTO roles. He works on AI agents, software architecture, developer tools, and efficient AI systems, and is one of the original authors of BRAID, a structured AI-reasoning approach.

Programs he can teach

  • AI Strategy for Executives
  • AI-Assisted Development for Software Teams
  • Designing AI Agent Systems
  • Product Development with Generative AI
  • From Prompt Engineering to Agent Engineering
  • Software Architecture & Technical Leadership
  • Speed, Quality & Cost Optimization in AI Systems
  • AI Transformation for CTOs & Technology Leaders

Numan Selman Duman

Numan is a developer and trainer working in software development and software-engineering education. He builds production systems using JavaScript, MongoDB, event-driven architectures, and end-to-end web applications, with a GitHub portfolio spanning e-commerce, booking systems, MongoDB aggregation pipelines, and Vue.js-based projects.

Programs he can teach

  • Modern Software Development with JavaScript
  • Frontend Development with Vue.js
  • Backend Development with Node.js & Express
  • MongoDB & Data Modeling
  • Full-Stack Web Application Development
  • AI Tools for Developers
  • AI-Assisted Coding, Testing & Debugging
  • Problem-Solving & Project Development for Junior Engineers

Mehmet Perk

Mehmet is a product leader, innovation consultant, and three-time technology founder with more than 15 years of experience. As an innovation consultant at Siemens, he works on digital transformation and innovation strategy for large organizations, having previously served as a product manager in Siemens' mobility division. All three of his own companies became profitable, with two ending in a successful exit. He teaches on product strategy, user experience, innovation, and new business models.

Programs he can teach

  • Product Management in the Age of AI
  • AI Product Strategy & Roadmapping
  • From Idea to Product: New Product Development
  • Product Discovery & User Research
  • User Experience for Product Managers
  • Innovation Management in Large Organizations
  • Identifying AI Use Cases
  • Turning Emerging Technology into a Business Model
  • Strategic Thinking for Product Teams

Gülnur Bayhan Deniz

Gülnur is Coyotiv's Engineering and Product Director and an experienced technology and product leader. Across her career she has worked as a software developer, QA engineer, solutions architect, business analyst, project manager, and Agile coach before moving into product development at startups. Today she leads cross-disciplinary, self-organizing engineering teams and focuses on integrating AI into engineering and product processes.

Programs she can teach

  • AI Literacy for Executives
  • AI Productivity for Teams
  • Engineering Management in the AI Era
  • Product Thinking for Technical Teams
  • Product Discovery & Experiment Design
  • Software Development Processes, Idea to Delivery
  • Software Quality & Process Improvement
  • Leading Cross-Disciplinary Teams

Deniz Kaynak

Deniz is a journalist, editor, and communications strategist. She's Editor-in-Chief at Aposto and leads marketing at Coyotiv, producing work on technology, AI, media, culture, and business, and specializing in turning complex subjects into clear, credible narratives. Her focus areas include content production, editorial decision-making, brand voice, and the role of human creativity in the AI era.

Programs she can teach

  • AI for Communications Teams
  • AI-Assisted Research & Content Production
  • Editorial Strategy in the AI Era
  • An AI Playbook for Brands
  • Protecting Brand Voice While Using AI
  • Research, Verification & Sourcing
  • Ethics & Trust in AI-Generated Content
  • Corporate Storytelling
  • Thought Leadership & Expert Content
  • Prompt & Workflow Design for Marketing Teams

Data & security

Built around your security policy, not the other way around

Corporate data deserves corporate-grade caution. Before any session begins, we agree exactly what's in scope.

01

Rules set before day one

We agree which tools will be used, what data can be shared, and where the boundaries sit — before any training begins.

02

No sensitive data on open systems

Sensitive company data is never shared with public or unsecured AI tools during a session.

03

Vendor-agnostic by default

We match tools to your existing security policy — ChatGPT, Claude, Gemini, Copilot, or a private/self-hosted model — rather than pushing one vendor.

04

Human oversight, always

Every program treats AI output as something to be reviewed, not trusted blindly — governance and human-in-the-loop practices are built into the curriculum itself.

Working with regulated or public-sector organizations?

We've delivered programs for public institutions and organizations handling sensitive population and workforce data (see our Network Intelligence case study). We're happy to work within your existing procurement, security review, and data-governance processes — just let us know what's required and we'll adapt.

How it compares

Where Coyotiv fits, compared to the usual alternatives

Generic e-learning One-off consultant Coyotiv
Tailored to your company & tools Rarely Often Always
Hands-on with your real workflows Rarely Sometimes Yes
Both business & technical depth Rarely Depends Yes
Support after the sessions end No Rarely Yes
Vendor-agnostic tool guidance No Depends Yes
Usable playbooks & templates you keep Rarely Sometimes Every program

Good to know

Frequently Asked Questions

Absolutely. We shape every program around participants' technical level. Our strong track record with technical teams also gives us an edge when your audience includes engineers.

Yes. Instead of running the same generic deck for everyone, we adapt the content to your industry, teams, tools, and needs.

Yes. We work in person, online, or hybrid — we'll figure out together which format works best for your team.

Anything from small focused teams to company-wide cohorts. We adjust the method and format to match group size.

Yes — we deliver programs in English or Turkish, and can accommodate other languages on request.

We take this seriously. Before training begins, we agree together which tools will be used, what data can be shared, and where the boundaries sit. We never share sensitive company data with open systems.

Yes — it's one of the areas where we're strongest. Follow-up sessions, mentoring, and applied support help teams carry what they learned into real day-to-day workflows.

Not always. Some programs can run without a license at all. Where one is needed, we help you choose the option that best fits the tools and security policies your company already uses.

Yes. Participants who complete a program receive a certificate of participation or completion.

We run short assessments at the start and end of the program, and track hands-on assignments and team feedback throughout. Where useful, we also define more concrete targets, such as time saved, adoption rate, or specific business outputs.

It depends on what you need. A single day can be enough to build awareness and give a team a solid starting point. But if you're aiming for lasting change and real integration into daily work, a hands-on program spread over several weeks is far more effective.

Get in touch

Let's design the right AI training for your team

In a 30-minute call, we'll:

  • Assess your organization's current level
  • Identify which teams to start with
  • Recommend the training format that fits you best
  • Sketch an estimated scope and timeline
We couldn't save your information. Please try again.
Your request has been received!