Senior engineers. Real production deployments. Every service is scoped to an outcome — not a sprint count.
Production AI agents with state machines, tool use, and human-in-the-loop checkpoints.
Most agent demos work once, in a sandbox, with no failure handling. We build tool-use agents on LangGraph, MCP, and CrewAI with LangSmith observability so you can actually operate them.
Custom MCP servers that give AI agents secure access to your data and tools.
Model Context Protocol servers let AI agents call your APIs, query your databases, and operate your tools — securely and observably. We build production MCP servers tailored to your stack.
AI agent strategy, readiness, and architecture — before any code is written.
Most AI product failures are strategy failures, not engineering failures. We help you decide what to build on proprietary data versus what you're renting from an API provider who'll ship the same feature in six months.
Cut AI agent and LLM infrastructure spend 40–60% without dropping capability.
Token volume is linear, margins are not. We audit LLM and agent spend by request type, then implement model routing, semantic caching, and prompt compression against quality baselines you can verify.
Adversarial testing for AI agents with tool access — before they hit production.
Agents with tool access have a far larger attack surface than chatbots. We run structured red team exercises and deliver remediation grounded in actual exploits, not theoretical checklists.
QA infrastructure for AI agent products — eval harnesses and prompt regression at scale.
AI-assisted dev ships code faster than manual QA can validate. We build eval harnesses, self-healing Playwright suites, and prompt regression detection so quality gates scale with output.
Conversational AI agents measured by resolution rate, not CSAT theatre.
We build chatbots and voice agents wrapped around state machines — intent taxonomies, RAG pipelines, ElevenLabs voice — wired to your knowledge base, escalation, and analytics.
Vision pipelines for multimodal AI agents — documents, video, operations.
A model hitting 94% mAP on your validation set will fail on Monday morning's shift-change lighting. We build vision pipelines that survive the actual distribution and feed into agent workflows.
MLOps for the models behind your AI agents — training, serving, drift, retraining.
Most models break between the notebook and production, then silently degrade. We build the MLOps stack — experiment tracking, inference serving, drift monitoring, retraining — under your agents.
Annotation and RLHF data for AI agent fine-tuning — by people who know the domain.
Model performance is decided at annotation time, not training time. We design annotation processes with IAA from batch one, distribution analysis, and RLHF preference workflows for LLM fine-tuning.
Wrap legacy systems with AI agents — strangler fig pattern, no rewrite required.
Your most valuable business logic is locked inside a system nobody wants to rewrite. We wrap it with AI agents using MCP and API facades — incrementally, without a multi-year migration.
Pre-investment due diligence on AI agent and LLM products — what works, what's smoke.
General software DD misses AI-specific failure modes — model drift, training data liability, demo-vs-production gaps. We run independent capability tests against your actual inputs before you close.
The web/backend layer your AI agents need to ship.
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.
APIs designed for AI traffic — high concurrency, structured failures.
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 for AI workloads — cost control, rollback, monitoring.
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.
Data pipelines that feed AI agents in production reliably.
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.
Flutter apps with on-device and cloud AI — for agent products that ship to phones.
On-device inference is no longer a tradeoff — it's an architecture choice. We build Flutter apps running TFLite, Core ML, and MediaPipe locally, and hit cloud LLMs for everything else.
Anonymized engagements with real metrics — no client names per NDA.
72%
Processing Time Reduction
94%
Classification Accuracy
1.2 days
Avg Processing Time
“The classification consistency was the biggest operational win. Adjusters are now working from standardised severity assessments rather than making independent calls on damage they have never seen before.”
— Head of Claims Operations, Motor Insurance Division
82%
Time Reduction
93%
Extraction Accuracy
2.8x
Review Throughput
“We were spending six hours per contract on review work that should have been automated years ago. The extraction accuracy is high enough that our lawyers now start from the AI output and spend their time on the obligations that actually require judgement.”
— Managing Associate, Corporate Practice Group
2.1x
Time on Page
38%
More Qualified Leads
52%
Fewer No-Shows
“The no-show reduction was the metric our agents cared about most. The buyers who book visits after exploring the virtual tour have already self-selected — they know the property and they are serious.”
— Head of Digital, Real Estate Platform
A 30-minute scoping call costs nothing. We will tell you exactly what to build and what it will cost — before any contract.
Fordel Studios