Vibe-coded solutions we can stake our reputation on
We leverage vibe coding wherever it fits, but only after explicit client sign-off.
Anyone can build a makeshift app now by prompting agentic coding tools in plain English. Few can make it production-grade, scalable, and armed with security safeguards to the teeth. What sets those few apart? Expertise honed 25+ years, a nose for nuance refined over countless projects, and an instinct for what works – *instinctools’ vibe coding service teams have all three in spades.
Vibe coding is the catchy name used for agentic engineering, where AI agents draft code, tests, and glue logic while engineers curate them. We’ve been practicing this model since 2024, putting autonomous coding assistants to work throughout the entire SDLC.
At our AI coding company, vibe coding is done to an enterprise standard:
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We leverage vibe coding wherever it fits, but only after explicit client sign-off.
Pressure-test your ideas in days. There’s little point in using traditional prototyping methods when swarms of AI agents can do the same work just as smoothly and at greater speed.
Build confidence in your initiative faster. Gone are the days of locking up a team for a quarter for just an MVP. Vibe coding lets a pair of coders do the heavy lifting in several weeks.
Build, upgrade, or scale your digital product without siphoning off time, talent, and capital, or grinding your operations to a halt. Peerless code quality guaranteed.
Well aware of the sheer number of vulnerabilities that can be spawned by LLMs or sneak in through a developer’s environment, we approach security as a system of its own, intricate and deeply interlocked. Your product unfolds within an infrastructure enveloped by a network of safeguards for data security, privacy, and compliance that leaves little to chance.
We develop and test software leveraging options from our legally approved list of safe AI models and integrated development environments, selected to match your budget while still giving you the features you need. All deployments run on secure private clouds or locally, whichever you prefer. Plus, your data isn’t used for training except if you choose to share it for model fine-tuning.
Unless security is explicitly in the prompt, it’s usually ignored. That’s just how LLMs work. Instinctools’ developers know how to speak their language, and it’s not just “write me a secure login function” and call it a day. We provide a detailed, language-specific security guidance referencing MITRE or OWASP error lists.
Once code is written, it undergoes automated reviewing through optimized SAST tools like Semgrep, CodeQL, or SonarQube.
Every line of AI-generated code carries a shadow of its training data. To keep that shadow from crossing legal or ethical lines, we are:
When pitfalls are acknowledged and carefully managed, the rewards become richer than anticipated. Maintaining your risk posture flawless, we make sure every bit of upside is yours to keep.
AI agents shave 20-30% off the coding time on average, and for well-scoped tasks, developers can move at nearly double speed. Just imagine how much faster you could go from a half-baked idea to an MVP to a full-scale launch.
Traditionally, building a relatively straightforward application still required frontend, backend, QA, and DevOps specialists collaborating for months. Once a job for a full team, such an app can now be built in a matter of weeks or even days by a single seasoned developer at the helm of a single- or multi-agent system. Less effort, less spend, expected results.
Run parallel market experiments, kill the duds early, and double down on the one concept customers actually want, turning “what if” into validated revenue before competitors even finish writing specs.
You’re right to question whether AI tools can develop software without planting landmines. Reddit is full of vibe coding horror stories, which are often very real. When your initiative is left to the inexperienced, whatever can fail, will fail. With *instinctools, your projects are in the hands of senior engineers who know all the cracks and how to bridge them.
| What you think can go wrong | How we prevent it from happening |
| “Saving time now means bigger maintenance bills later.” | The good news is that agents are just as good at maintaining code as they are at writing it. But more often than not, tasking AI to craft a compliant replacement is cheaper than reworking legacy code from the ground up. Legacy modernization isn’t the headache it used to be. |
| “What if AI picks up security vulnerabilities and bad habits it has seen in its training data?” | It certainly can, but we make sure nothing like that gets into production, working in secure-by-design backend systems with built-in vulnerability scanning and guiding agents to follow only industry best practices. |
| “If someone jailbreaks the model, our clients’ data will be at risk.” | AI code never hits stage or production without first being put through automated unit tests, dependency checks, and security scans. Our engineers also make sure agents ignore instructions from blacklisted tools. |
| “How about spaghetti code, inconsistent formatting, hallucinated APIs, phantom dependencies, and all the other messy ‘vibes’ of vibe coding?” | Well begun is half done. When agents are properly onboarded, with a thorough development workflow, a defined development approach, coding standards, clear boundaries, a high quality bar, and an unambiguous project context, technical debt is kept at bay. |
Getting production-grade results through vibe coding still comes down to good-old development chops, just applied differently. Our top engineers have cracked the code, pivoting on a dime with every novelty in the AI’s ever-expanding toolbox.
Our engineers spent months neck-deep in the guts of toolchains, figuring out where each model shines, probing their quirks, and stitching them together, which resulted in our own technology-agnostic, configurable middleware infrastructure. All the dots linking potentially useful tools are already in place, so now we can spin up an agentic setup for clients in hours. No out-of-the-box solution, be it Claude Code, Codex, or Cursor, can match that kind of development environment without a long, messy phase of trial-and-error tuning.
Sometimes a breakthrough is also a homecoming. Vibe coding may feel new, but it only throws the spotlight back on what’s timeless – solid solution architecture. We design it with mastery, charting requirements, mapping implementation routes, and making sure every piece locks into the larger business whole.
With vibe coding, output quality lives and dies by precision, depth, and discipline in prompts. Well-versed in modern LLMs’ capabilities and their context limits, *instinctools’ engineers carefully decompose tasks to fit perfectly within those boundaries. Even for large specifications, using a top-down decomposition approach allows us to eliminate context loss and ensure the models execute the described requirements down to the last detail.
As every request, no matter how simple, invokes the whole model, token consumption must be constantly tracked to prevent unintended cost creep. We do this using tools like OpenTelemetry and Langfuse, and by setting up custom token-usage alerts that trigger whenever a specified threshold is exceeded.
Agents have their little pathologies, and human-imposed rules are the prescription. Some tend to loop endlessly on dead-end tasks, so we set up rules like “Three strikes on a problem, and you stop and alert me.” Others jump onto unrequested sub-tasks before finishing the main assignment. In such cases, we instruct them to stay locked on the main task until it’s completed.
What really sets it apart is how the developer’s role changes, from writing code using a programming language to orchestrating in a natural language the way code gets written.
Only when a seasoned engineer has steered its generation process, tuning performance, enabling scalability, addressing security gaps, and applying thorough testing to every line upfront.
Could be ready in a week. Timing depends on your requirements, which we clarify in detail before kickoff.
You own 100% of the IP. We make sure oversight and governance are structured to give you full visibility and control.
More than real, they are senior and principal engineers who live and breathe AI innovation.
Anything from insecure design, broken access control, and user authentication errors, to supply chain attacks, unless prevented with pragmatic guardrails.
We use the tools that fit each task best: Claude Code excels at programming, Codex shines at code review, GPT 5 and Claude Opus – at planning, and we bring in other AI coding tools as needed, except when clients request a specific IDE.
