AI Agents and Custom AI Products, From Idea to Launch
Bespoke AI products with real planning, real tool use, and real guardrails.
DIRECT LINE — +92 314 7046916
Most "AI agent" projects are a wrapper around a single LLM call labeled as autonomous. Real agents — ones that plan, use tools, recover from failures, and stay within scope — require deliberate engineering. The same is true for custom AI products: the model is 30% of the work; the surface around it is the rest.
We build agents and AI products as full systems: a planner, a set of well-defined tools, structured outputs, evals, and clear escalation paths when the agent is uncertain. We start with the narrowest possible task that delivers real value, validate it with evals, and only then expand the agent surface.
- 01Agent architecture with planning + tool use + memory
- 02Tool layer with structured inputs/outputs and validation
- 03Eval harness with golden examples and regression tests
- 04Guardrails: scope limits, cost limits, escalation triggers
- 05Observability for agent runs (traces, decisions, tool calls)
- 06Custom AI product UX (chat, command bar, embedded, or ambient)
- 07End-to-end product launch including auth, billing, and analytics
- Anthropic Claude, OpenAI
- LangGraph, custom agent loops
- Function/tool calling, MCP
- Promptfoo, Braintrust
- Next.js / React for product UI
- PostgreSQL + pgvector
ATS Resume Helper
AI resume scoring + builder we built and shipped.
Visit ATS Resume Helper ↗REAL PROJECTS, ANONYMIZED ON REQUEST.
Most of our work is under NDA. Reach out for a walkthrough of relevant projects in ai agents & custom ai products — we will share scope, architecture, and outcomes for engagements that match yours.
[ REQUEST A WALKTHROUGH ]QUESTIONS WE GET A LOT.
When does an agent make sense vs a simple prompt?
When the task requires multiple steps, real-time decisions, or tool use to gather information. If a single prompt with retrieval can do it, do not build an agent. Agents add cost, latency, and failure modes.
How do you stop the agent from going off the rails?
Scope limits in the system prompt, hard caps on iterations and cost per run, structured outputs that gate every action, and escalation to a human when confidence is low. The guardrails are not optional — they are the product.
What is MCP and should we use it?
Model Context Protocol is an open standard for giving LLMs access to tools and data sources. It is worth using when you want the same toolset to work across Claude, ChatGPT, and other clients. For internal-only agents, a custom tool layer is often simpler.
Can you launch an AI product end-to-end?
Yes. We have shipped Acadly AI and ATS Resume Helper as full products with auth, billing, analytics, and ongoing iteration. We can do the same for your concept.