Fordel StudiosAI Development Services for Production Systems
Senior engineers. Real production deployments. Every service is scoped to an outcome — not a sprint count.
AI Agent Development
Agents that ship to production — not just pass a demo.
Most agent demos work once, in a controlled environment, with no failure handling. We build tool-use agents with LangGraph state machines, MCP servers, and CrewAI pipelines — with LangSmith observability and human-in-the-loop checkpoints so you can actually operate them.
AI-Powered Testing & QA
Test infrastructure that doesn't break when your dev velocity doubles.
AI-assisted development ships code faster than manual QA can validate it. We build QA infrastructure — LLM-generated test scaffolding, self-healing Playwright suites, Chromatic visual regression, and LangSmith eval harnesses — so your quality gates scale with output. Built for teams using Cursor, Copilot, or any LLM-in-the-loop workflow.
AI Product Strategy
Find where AI creates a moat. Skip the rest.
Most AI product failures aren't engineering failures — they're strategy failures. We help you identify which AI investments build on proprietary data or workflow depth versus which ones you're renting from an API provider who'll ship the same feature in six months.
API Design & Integration
APIs designed for AI agents first, human developers second.
AI agents fail at the API layer more often than the model layer — ambiguous schemas, inconsistent errors, and undocumented edge cases are the usual culprits. We design APIs spec-first using OpenAPI 3.1 and MCP tool schemas so they work reliably for both agent tool-calling and human developers from day one.
Cloud Architecture & DevOps
AI infrastructure sized for what you actually run, not what you might.
Most teams overpay for inference because they sized for peak and priced for always-on. We design cloud infrastructure around your actual request patterns — right-sized compute, self-hosted model serving where it pencils out, and cost controls that catch drift before it hits the bill.
Computer Vision Solutions
Vision models validated against production conditions, not held-out test splits.
A model that hits 94% mAP on your validation set and fails on Monday morning's shift-change lighting is a benchmark artifact, not a production system. We build and validate computer vision pipelines against the actual distribution they'll encounter — lighting variation, occlusion, camera drift, and the edge cases your training set doesn't cover.
Data Engineering & Analytics
The data foundation your AI actually needs to work in production.
Most AI projects fail at the data layer, not the model layer. We build dbt transformation pipelines, Airflow/Prefect orchestration, and feature stores that make training/serving consistency a structural guarantee — not a debugging exercise. For teams running ML in production or preparing to.
Full-Stack Engineering
AI-native full-stack engineering — built for streaming, agents, and scale.
AI tools accelerate scaffolding. They don't build streaming renderers, agent state timelines, or LLM error boundaries — the frontend patterns that make AI features feel production-grade. We build full-stack products where AI integration is designed in from day one.
Machine Learning Engineering
MLOps from notebook to production — and six months after.
Most models break between the notebook and production, then silently degrade after launch. We build the full MLOps stack: experiment tracking, inference serving, drift monitoring, and automated retraining pipelines. Built for teams shipping real models, not demo projects.
Mobile Development
Flutter apps with on-device AI — latency, privacy, and real-time UX built in.
On-device inference is no longer a trade-off — it's an architecture choice. We build Flutter applications that run TFLite, Core ML, and MediaPipe locally for latency-sensitive features, and hit cloud LLMs for everything else. Right tool, right layer, every feature.
Natural Language Processing
Pick the right NLP architecture — SLM, spaCy, or LLM — for every task.
Modern NLP has two cost regimes: LLMs for complex reasoning and open-ended generation, fine-tuned SLMs for high-volume classification and extraction. We design systems that match architecture to task so the unit economics hold at scale.
AI Cost Optimization
LLM spend audited, routed, cached, and cut.
Teams scaling AI products on OpenAI or Anthropic APIs often hit a unit economics wall before they see it coming — token volume is linear, margins are not. We audit your LLM spend by request type and model, then implement model routing, semantic caching, and prompt compression against quality baselines you can verify. Built for engineering teams with real production traffic, not PoC workloads.
AI Safety & Red Teaming
Find what breaks your AI system before adversarial users do.
Prompt injection, jailbreaking, indirect injection via RAG retrieval, adversarial classifier inputs — agentic systems with tool access have a substantially larger attack surface than pure text generation. We run structured red team exercises against your AI systems and deliver remediation plans grounded in actual exploits, not theoretical checklists. Built for teams shipping LLM-based products to production.
AI Training & Data Annotation
Annotation quality is model quality. We treat it that way.
Model performance is decided at annotation time, not training time. We design annotation processes with IAA measurement from batch one, production-distribution analysis, and RLHF preference workflows for LLM fine-tuning. Built for teams shipping models to production, not demos.
Conversational AI & Chatbots
Voice agents, multimodal inputs, resolution logic — not just fluent responses.
Conversational AI that's measured by resolution rate, not CSAT. We build intent taxonomies, RAG pipelines, and voice agents using ElevenLabs and PlayHT — wired to your knowledge base, escalation platform, and analytics stack. The right build for support teams handling 1,000+ monthly conversations.
Figma to Code
Figma to production — not a prototype that needs a rewrite.
v0, Bolt, and Lovable generate prototype-quality code fast. What they don't produce: ARIA semantics, design system tokens, full component states, or passing Core Web Vitals. We take designs from Figma to production-ready React — the first time.
Legacy AI Augmentation
Add AI to production systems without touching what works.
Your most valuable business logic is probably locked inside a system nobody wants to rewrite. Using the strangler fig pattern and API facades, we wrap legacy systems with document AI, intelligent routing, and workflow automation — incrementally, without a multi-year migration. Built for companies where replacing the core system isn't an option.
Technical Due Diligence
AI due diligence that tests what you're actually buying.
General software due diligence misses the failure modes specific to AI systems — model drift, training data liability, and the gap between a vendor demo and production performance. We run independent capability tests against your actual inputs before you close.
Vibe Code to MVP
Your Cursor prototype, production-hardened and shipped.
Cursor and Claude produce working prototypes fast — but they ship with open CORS, committed secrets, and authentication that doesn't hold up. We audit the codebase, fix what's broken, and deploy to production with CI/CD, monitoring, and real auth. Built for founders who have something working and need it to be real.
Not sure which service fits?
A 30-minute scoping call costs nothing. We will tell you exactly what to build and what it will cost — before any contract.