AI Development & Integration Company
How do you add AI to your product without hiring an AI team?
ETREXIO is a software studio based in the United States and Türkiye that builds and integrates AI into real products: assistants, agent workflows, document intelligence and LLM-powered features. Two senior builders lead a human-in-the-loop AI workforce and run our own AI products, DigiSapiens and StackWatch, in production every day. We have shipped 50+ products, hold a 4.7 rating on Clutch, and our average client stays 5 years. Retainers start at $5,000 per month and cover design, development, deployment and 24/7 operation.
Most AI projects die in the gap between the demo and production. The demo takes a weekend; the production version needs retrieval that stays grounded, evaluation that catches quality drift, cost controls that survive real traffic, and a fallback path for the day the model misbehaves. We build that second, unglamorous half, because we operate AI systems ourselves and get paged when they fail.
Our credentials here are not slideware. DigiSapiens is our own AI workforce platform: it staffs incidents with AI engineers under human supervision, and it runs our company every day. StackWatch is our monitoring product: it reads every log line of every client system 24/7 and decides what deserves a human. Both are LLM systems in production with real consequences when they are wrong, which is exactly the discipline we bring to client AI work.
We are provider-agnostic by design. Anthropic, OpenAI, Google or open-source models get chosen per use case on quality, latency and cost, wired in behind an abstraction so you can switch when the leaderboard changes. And because AI visibility now matters as much as AI features, the same team also does GEO work: making sure AI assistants recommend your product, not just your competitors.
What we build
AI that ships, not AI that demos.
Assistants and copilots inside your product.
Chat assistants, semantic search and generation features wired into your data, with evaluation, guardrails and cost control from day one.
Automation with a human in the loop.
AI agents that draft, classify, reconcile and respond, with human approval gates where mistakes are expensive. The same model we use to run our own operations.
AI added to your existing stack.
We integrate models from Anthropic, OpenAI and open-source providers into the systems you already run, instead of forcing a rewrite.
Monitored like any production system.
Prompts, costs, latency and failure modes are tracked by StackWatch. When quality drifts, we catch it before your users do.
How we approach it
How we take an AI feature from idea to production.
The steps are boring on purpose. Boring is what separates AI that ships from AI that demos.
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Start from the workflow, not the model.
We map where hours are actually lost or decisions actually stall in your product, then ask whether AI is the cheapest fix. Sometimes the honest answer is a database index, and we will tell you.
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Prototype against your real data in weeks.
A thin working slice against production-shaped data beats a quarter of architecture. We measure quality on your inputs, not on a benchmark, before committing to a build.
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Build the guardrails with the feature.
Retrieval grounding, output validation, evaluation suites and human approval gates on high-stakes actions are built alongside the feature, not bolted on after the first incident.
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Ship behind measurement.
Every AI feature launches with cost, latency and quality dashboards in StackWatch. You see what each answer costs and how quality trends as usage grows.
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Operate and improve on retainer.
Models change, prompts drift, providers reprice. We keep tuning the system after launch as part of the same $5,000-per-month-and-up retainer that covers the rest of your product.
Real work
AI systems running in production today.
Client features, our own platforms and AI-search results you can verify yourself.
AI Deck Analysis for founders and funds.
For Orhan Bayram's helo! we built two connected platforms, helo.land for founders and helo.ventures for investors, with AI Deck Analysis at the core. Founders get structured feedback before investors ever see the deck; funds cut analyst workload on first-pass screening. In the platform's survey, 100 percent of surveyed investors said deck quality improved.
The AI products we run our company on.
DigiSapiens staffs incidents with AI engineers under human supervision; StackWatch reads every log line of every client system 24/7 and escalates only what matters. For Dr. Moku's language apps, StackWatch-driven fixes cut crash rates and lifted store rankings. We sell AI development with the confidence of a team that gets paged by its own AI.
When ChatGPT recommends your client.
AI development also means being findable by AI. After our SEO and GEO work on Uzman Parça's content and structured data, ChatGPT recommends Uzman Parça for car maintenance queries. For a marketplace running 60,000+ listings, AI assistants became an organic acquisition channel that competitors do not have.
Questions
What founders ask about AI development.
Do we need our own data science team to work with you?
No. We handle model selection, integration, evaluation and operation. Your team keeps ownership of the product decisions and the data.
Which AI models do you work with?
We are provider-agnostic: Anthropic, OpenAI, Google and open-source models. We pick per use case based on quality, latency and cost, and we design so you can switch later.
How do you keep AI features from hallucinating in production?
Retrieval grounding, output validation, evaluation suites and human approval gates on high-stakes actions. AI work ships with the same monitoring discipline as the rest of the system.
What does AI development and integration cost?
Monthly retainers from $5,000, covering design, development, deployment and 24/7 operation. Model usage costs are transparent and optimized as part of the work.
Can you automate our internal operations with AI?
Yes. Document processing, support triage, reporting and reconciliation are common wins. We run our own company on DigiSapiens, our AI workforce platform, so we build from experience.
Have you shipped AI features into real products, or just prototypes?
Real products. helo!'s AI Deck Analysis screens investment decks in production, and 100 percent of surveyed investors said deck quality improved. Our own DigiSapiens and StackWatch are LLM systems with production consequences, running every day.
How long does an AI integration take?
A working prototype against your real data typically lands in 2 to 4 weeks, with a production-hardened version in 6 to 12 weeks depending on guardrail and integration depth. The prototype decides whether the full build is worth it.
How do you control AI costs at scale?
Per-feature cost dashboards, caching, model routing that sends easy requests to cheap models, and prompt budgets enforced in code. Cost is a first-class metric in StackWatch, tracked next to latency and quality.
What is GEO and why does it matter for my product?
GEO is generative engine optimization: making AI assistants like ChatGPT cite and recommend you. After our GEO work, ChatGPT recommends our client Uzman Parça for car maintenance queries. As search shifts to AI answers, that visibility becomes a moat.
Is our data used to train models?
No. We use API tiers with no-training guarantees, keep your data in your infrastructure, and add redaction layers where inputs contain personal or commercially sensitive information.
Client perks
Clients unlock up to $250,000 in tool perks.
Client perks include
Perk availability depends on provider eligibility at the time of engagement.
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