Instinctools Company

06/03/2026

When technical documentation grows past a handful of manuals, the cracks get expensive: duplicated effort, inconsistent outputs, version-control chaos, manual rework across channels.

DITA XML was designed to fix this at the architecture level. One source. Multiple formats: PDF, HTML5, EPUB. Content reused across products, languages, and teams — down to individual paragraphs and keywords.

The Instinctools team behind DITAworks explains the standard, its four core principles, and why most implementations still go wrong.

Struggling with scattered, inconsistent documentation? Learn how DITA XML brings structure to technical content with modular authoring, single-sourcing, and multi-format publishing.

06/02/2026

25 years in software development give you a particular perspective on how the industry works and where it tends to go wrong.

Alexey Spas, CEO and Founder of Instinctools, spoke with Goodfirms about culture, flexible engagement models, technology neutrality, and why most AI projects never reach production.

We picked five key ideas from that conversation and are sharing them here.

06/01/2026

O&G companies increased AI, gen AI, and agentic AI spending by 40% in 2025. BCG estimates that top adopters could lift EBIT by 30–70% over five years, while also reducing their carbon footprint.

The Instinctools AI CoE team broke down real-world applications across the full value chain from exploration and drilling to logistics, refining, and sales. Chevron, Shell, Aramco, bp — plus what's still holding the industry back.

Full breakdown at the link 👇
https://www.instinctools.com/blog/ai-in-oil-and-gas-industry/?utm_source=linkedin.com&utm_medium=referral&utm_campaign=casestudy

Explore key AI applications in the oil and gas industry: use cases in exploration, drilling, pipeline monitoring, and generative and agentic AI adoption for energy companies.

05/27/2026

Why do some engineering teams ship reliable software fast while others just stay busy?

Alexey Astakhov, VP of Engineering at Instinctools, calls it hyper-productivity — and in his recent piece for TechBullion he breaks it down into something concrete: architecture choices, developer experience, AI guardrails, psychological safety, and the way product and engineering actually work together.

We pulled the key ideas from the interview into cards. Swipe through

05/25/2026

Product roadmaps shift week by week. AI keeps rewriting priorities. Regulations tighten faster than teams can adapt.

Traditional engagement models weren't built for this pace. Fixed scope turns every change into a new contract. Staff augmentation hands team management right back to the client.

That's why Instinctools is introducing Managed Capacity, a dedicated cross-functional team of 3 - 8 specialists under a single monthly fee. We handle hiring, team management, delivery against KPIs, and compliance.

📖 Read the details here:

https://www.instinctools.com/managed-capacity?utm_source=linkedin.com&utm_medium=referral&utm_campaign=marketing&utm_id=casestudy

Scale without management drag. Our managed capacity model gives you dedicated pods of experts with SLA-backed delivery and KPI accountability.

05/22/2026

A recent project for a premium furniture brand: a suite of solutions designed to bridge the online and offline shopping experience.

What we built:

🔹Mobile app with LiDAR + AR
- reconstructs a room as a dimensionally accurate 3D model right from the customer's phone.

🔹Smart matching algorithm
- recommends optimal furniture sizes for the scanned space and proposes safe custom-size options when no catalog item fits.

🔹Web app for Holobox holographic displays
- lets customers rotate a sofa and switch finishes from a paired tablet right in the showroom.

The result:
+20% in online sales
+25% in CSAT

Discover how a luxury furniture retailer enhanced online and in-store shopping with AR room scanning, intelligent size recommendations, and holographic 3D product exploration, boosting sales and customer satisfaction.

05/21/2026

What actually makes an engineering team productive?

Not lines of code shipped, and not the trendiest stack. According to Alexey Astakhov, VP of Engineering at Instinctools, it's the combination of architecture, culture, and a mature approach to AI.

In his new piece for , he breaks down why hyper-productivity is a system rather than a team of superhumans and which frameworks keep engineering teams from breaking as they scale.

Speed at the team level requires the right architecture underneath it. Most organizations underestimate how much their technical foundation impacts delivery efficiency. Partnering with an artificial intelligence development company in USA can help organizations audit the following foundations to ens...

05/19/2026

The global rating platform 50Pros has named Instinctools Company among the world's leading IT companies, specifically highlighting our work on enterprise-grade AI solutions.

Why does this matter?

Behind this recognition are dozens of delivered projects and a team of engineers who turn complex technology into tangible business outcomes every day. Industry acknowledgment like this is, first and foremost, a credit to our clients, the ones who trusted us with ambitious challenges.

Thank you for that trust!

05/18/2026

GoodFirms just published an interview with Alexey Spas, CEO of Instinctools. We share some ideas from the interview on why we want to stay proactive in a shifting tech landscape, how we build long-term, trust-based client relationships, and how we make AI practical in real business environments.

Read on, and tell us what resonates most.

Find trusted Instinctools reviews & rankings on Goodfirms. Explore the company profile, top-notch services, and client testimonials to evaluate for your next project.

05/13/2026

Our CEO Alexey Spas has published his first piece on Forbes.

🤖 In it, he picks up a question that's quietly becoming central for almost every enterprise working with AI: how much autonomy are we actually ready to give these agents, and who's accountable when something goes off?

His argument: the answer is a control layer between agents and your business systems. One that defines what an agent is allowed to do, what it has to prove before doing it, and how to recover when it doesn't.

📖 Worth a read:

As agentic AI became the norm, many companies treated control as something to work out later, but these agents have introduced new operational risks.