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App Corp
Full-service software engineering
Engineering your experience…
App Corp
Full-service software engineering
Engineering your experience…
Ship AI agents that actually work in production
Multi-step reasoning agents, LLM orchestration pipelines, and autonomous systems that handle edge cases, scale under load, and integrate cleanly into your existing stack — not demos that break on real users.
8+
AI agents deployed
85%
Avg. autonomous resolution rate
6 wks
Avg. implementation time
60%
Avg. operational cost reduction
Most AI agent demos break the moment real users touch them. I build the ones that don't.
I design and deploy production-grade AI agents, LLM pipelines, and autonomous systems for startups and businesses that need AI to do real work, not just generate text in a sandbox. Every system ships with proper error handling, fallback logic, observability, and cost optimisation so you are not burning through API credits.
From multi-step reasoning agents to tool-calling workflows, I architect systems that handle edge cases, scale under load, and integrate cleanly into your existing stack. Every engagement starts with your business problem, not the technology — we define what the agent needs to accomplish, map the decision tree, and build iteratively with real data.
Multi-step reasoning agents that execute complex tasks autonomously — data extraction, decision-making, customer interactions, and workflow orchestration. Built with proper state management, retry logic, and human-in-the-loop escalation.
Production pipelines using OpenAI, Anthropic Claude, Google Gemini, or open-source models (LLaMA, Mistral). Prompt management, model routing, fallback chains, and latency optimisation for real-time applications.
Agents that interact with APIs, databases, and third-party services through structured function calling. Dynamic tool selection, parameter validation, and error recovery so agents reliably complete their tasks.
AI-powered chat and voice interfaces with persistent memory, context management, conversation history, and guardrails. Multi-turn dialogue with topic switching and intent disambiguation.
End-to-end automation that replaces manual processes — document processing, data entry, report generation, approval workflows, and notification triage. Built with audit logging and human approval gates where needed.
Token usage tracking, cost per query monitoring, latency dashboards, and automated budget alerts. Prompt optimisation, model tiering, and caching strategies that keep AI operational costs predictable and sustainable.
Map the agent's decision tree, define success criteria, and identify failure modes before building. We produce an Agent Specification Document that covers the task scope, tool requirements, error recovery strategy, and evaluation methodology.
Build a working prototype with your real data and use cases. Run structured evaluation against ground-truth examples, identify failure patterns, and iterate on prompt design, tool selection, and retrieval strategy before committing to production architecture.
Production hardening: error handling, fallback logic, rate limiting, observability instrumentation, cost tracking, and comprehensive test coverage. The agent that worked in a notebook now works under real-world conditions.
Deploy to production with staged rollout, monitor real-world performance, and optimise based on production traffic. Includes a 90-day post-launch period with ongoing accuracy monitoring and cost optimisation.
EduPilotPro needed an AI attendance agent that could autonomously process natural language queries, cross-reference student records with attendance logs and academic policies, execute database lookups, and return cited answers — all while handling ambiguous queries, missing data, and policy exceptions without human intervention.
We built a LangGraph-based agent with tool-calling capabilities connected to the EduPilotPro database schema. The agent used a multi-step reasoning loop: parse intent, retrieve relevant records, cross-reference with policy documents, validate against edge cases, and format response with citations. A confidence scoring system determined when to escalate to a human operator.
Many of our best projects combine two or more services from the list below.
Turn your data into an AI that actually knows your business
A production-ready healthcare operations platform deployed in 12 weeks
A production-ready fitness operations platform deployed in 12 weeks
Let's build something great
Book a free 45-minute scoping call. Walk away with a clear picture of what we would build, how long it would take, and how much it would cost — regardless of whether you move forward.
Average response time: 2 business hours. No commitment required.
"App Corp delivered a production-ready AI platform in 11 weeks that our internal team estimated would take 9 months. The architecture is clean, the docs are thorough, and the product works exactly as specified."
Emily Park
CTO, PetScreening
50+
MVPs
98%
Retention
10wk
Avg. launch