Frequently asked questions

$15K–$80K for most custom builds; AI automation workflows start lower, around $8K–$20K. We don’t bill hourly — we scope a fixed price per phase, so you know the total before signing anything.

Fixed price, per phase. We break the work into scoped phases, quote each one, and hold to it. If scope changes mid-build we re-quote the remaining phases and you approve before we continue — no surprise invoices.

Book a 30-minute call. Our CEO talks through your project, gives you a timeline and price range, and is straight about whether we’re the right fit. No pitch deck, no sales funnel.

Weekly. We ship working software every sprint, so you review real product instead of status decks. The people on your scoping call are the people writing your code.

Yes — we’ll sign before the scoping call if you want. Your IP is yours: source code, designs, and documentation. We never reuse client code or share proprietary architectures.

Yes — it’s one of our most common projects. We audit what exists, decide what to keep versus rebuild, and give you an honest assessment, even when that means starting fresh.

We conduct a thorough technical discovery to define the project scope. We then offer predictable pricing models, either as a fixed-scope engagement or a dedicated sprint team, based on your specific needs.

You do. Upon completion and final payment, your company retains complete ownership of all source code, technical documentation, and deployed infrastructure.

Yes. A major part of custom development involves building secure middleware and APIs to ensure your new software communicates perfectly with your current enterprise systems.

No. We enforce absolute data isolation. We utilize enterprise-tier APIs with strict zero-data-retention policies, or we deploy dedicated, self-hosted open-source models inside your own Virtual Private Cloud (VPC).

RAG connects an AI model to a secure database of your files, allowing it to read your data before answering. Fine-tuning actually alters the model's internal neural weights. We typically architect RAG pipelines for B2B knowledge retrieval, as it is highly accurate, easily updatable, and mathematically reduces hallucinations.

We implement semantic caching layers, strict rate limiting, and intelligent model routing. Complex reasoning queries go to large models and simple, repetitive queries go to cheaper, faster micro-models to drastically optimize your cloud expenditures.

For 85% of B2B and SaaS platforms, React Native provides the exact same user experience at significantly lower development cost. However, if your application relies heavily on complex 3D rendering, intense background processing, or proprietary Bluetooth hardware, we will enforce a strict Native (Swift/Kotlin) architecture.

Yes. Apple and Google have notoriously strict compliance guidelines. We manage the entire lifecycle, from generating provisioning profiles and certificates to handling privacy manifests and pushing the final binary.

We utilize an offline-first architecture. User actions are securely recorded in a local device database (SQLite). Once the operating system detects a stable network connection, a background sync pipeline automatically resolves conflicts and pushes the data to your cloud servers.

Yes. We highly recommend utilizing established headless UI libraries. We theme and customize these libraries natively in Figma to match your brand, which drastically accelerates your engineering team's build time by avoiding custom CSS overrides.

B2C prioritizes emotional engagement and aesthetic novelty. B2B software prioritizes data density, operational efficiency, and task completion. Enterprise users use your software for 8 hours a day; they need predictable layouts, fast data tables, and high-contrast accessibility, not flashy animations that slow them down.

A design token is a semantic variable for a design decision. Instead of hardcoding the hex color #0F172A in 50 different places, we create a token called color.background.primary. If you rebrand in two years, the engineers only update the core token, and the entire application updates instantly.

For early-stage products, standard container services (like AWS ECS or Google Cloud Run) are highly effective and require less operational overhead. We only recommend Kubernetes when your application requires complex microservice orchestration, highly aggressive auto-scaling, or multi-cloud redundancy.

We are cloud-agnostic. AWS offers the deepest enterprise ecosystem. Google Cloud (GCP) provides superior developer experience and native data-science tooling. Microsoft Azure is ideal if you are deeply entrenched in the enterprise Windows ecosystem. We architect for what suits your specific technical constraints.

We implement the principle of least privilege via strict IAM roles, enforce database encryption at rest and in transit, configure private subnets for all core data, and automate vulnerability scanning within the CI/CD pipeline to establish a compliant security baseline.

An LLM (like GPT-4) is simply a text-prediction engine. An AI Agent is a software system that uses an LLM as its reasoning engine to write code, decide which internal tools to use, and execute real-world actions (like updating a Jira ticket or sending a Slack message) to accomplish a specific goal.

We utilize strict Function Calling (JSON schemas). The LLM is technically incapable of directly modifying your database. It can only output a pre-approved JSON structure requesting an action. Our deterministic middleware validates that JSON payload against your strict business rules before allowing the actual API execution.

We evaluate based on project complexity. We utilize LangChain and LangGraph for complex, stateful multi-agent workflows that require cyclical logic. For simpler execution paths, we use lightweight custom Python orchestrators to minimize latency and cloud compute costs.