Custom LLM apps & AI agents
Most AI features are a thin wrapper around an API call that breaks the moment a real user does something unexpected. We build LLM applications with proper orchestration — retries, guardrails, evals, and observability — so they behave the same on day 90 as day 1.
What you get
- Agentic workflows with tool-calling and multi-step reasoning
- Guardrails, structured outputs, and evaluation harnesses
- Token-cost instrumentation and latency budgets
- Works with OpenAI, Anthropic, and open models
How we deliver.
A structured engagement from discovery to deployment — no surprises, no scope fog.
Discovery & scope audit
We map your business process end-to-end, identify which steps benefit from LLM reasoning, and define success metrics — accuracy targets, latency budgets, and cost ceilings — before writing a single prompt.
Prompt engineering & model selection
We design structured prompt chains, evaluate model options (GPT-4o, Claude, Llama, Gemini), and benchmark them against your real data to pick the best cost-to-quality ratio.
Tool orchestration & agent architecture
We wire the agent into your existing systems — APIs, databases, CRMs — with proper tool-calling schemas, retry logic, and fallback paths so the agent can act autonomously.
Guardrails & evaluation harness
Before launch, we build a test suite of real-world edge cases and deploy guardrails (content filters, output validators, and hallucination detectors) that keep the agent on-rail.
Deploy, observe, iterate
We ship to production with full observability — token-cost dashboards, latency traces, and failure alerts — then iterate on real-user feedback to continuously improve accuracy.
Where this applies.
Internal knowledge assistants
AI agents that answer employee questions from internal docs, SOPs, and Confluence — with source citations and access controls.
Customer-facing support bots
Multi-turn conversational agents that resolve Tier-1 support tickets, escalate complex issues, and log structured handoff summaries for human agents.
Document processing pipelines
Automated extraction and classification of invoices, contracts, and applications — turning unstructured PDFs into structured database records.
Sales intelligence agents
Agents that research prospects, enrich CRM records, draft personalized outreach, and score leads based on configurable criteria.
Technical depth.
Multi-step reasoning chains
We decompose complex tasks into discrete, testable steps — each with its own prompt, validation, and retry logic — so failures are isolated and debuggable.
Structured output enforcement
Every LLM call returns typed, validated JSON using Pydantic models or Zod schemas. No more parsing free-text responses and hoping for the best.
Model-agnostic abstraction layer
We architect a provider abstraction so you can switch between OpenAI, Anthropic, or open-source models without rewriting business logic.
Cost & latency instrumentation
Per-request token tracking, cost attribution by feature, and latency percentile dashboards so you always know where budget is going.
Common questions.
Q.Which LLM providers do you support?
We work with OpenAI (GPT-4o, o1), Anthropic (Claude 3.5/4), Google (Gemini), and self-hosted open models (Llama, Mistral). We help you pick the right model for your cost and accuracy constraints.
Q.How do you prevent hallucinations?
Through grounding — retrieval-augmented generation, structured outputs, fact-checking chains, and confidence scoring. We also build evaluation suites that measure accuracy against known-good answers.
Q.Can you integrate with our existing systems?
Yes. We design agents to call your APIs, query your databases, and push results into your CRM, ERP, or ticketing system via webhooks or direct integration.
Q.What does ongoing maintenance look like?
Post-launch, we provide 30 days of included support. For ongoing work, a monthly retainer covers prompt tuning, model upgrades, and performance optimization as your data evolves.
Featured projects.
AI infrastructure cleanup: cost & reliability overhaul
A production AI system that was slow, over-budget, and failing silently — audited, root-caused, and rebuilt into something dependable.
Chrome Extension for automated CRM lead extraction
A Manifest V3 browser extension that lets sales representatives clip prospect contacts from web directories directly into their CRM with a single click.
Cross-platform mobile app for real-time logistics tracking
A high-performance React Native / Expo app that lets field operators track inventory and log tasks in real time, even while offline.
What our partners say.
“Our staff spent hours searching files, and our early AI bot just hallucinated answers. Lesscode rebuilt our RAG pipeline with precision embedding and citations. Accuracy went to 99%, and data leaks are zero. Stellar work.”
“The AI voice receptionist Lesscode built qualified and booked over 230 jobs in our first month. We no longer miss calls after hours, and GHL scheduling syncs perfectly. Highly recommended.”
“Our AI token costs were out of control and queries were failing silently in production. Lesscode did an audit, diagnosed three critical bottlenecks, and completed the refactor. Costs dropped by 63% and response times are now sub-second.”
“Their workflow automations connected our CRM, billing, and reporting tools via n8n and Python scripts. What used to take hours of manual copy-pasting is now fully hands-off and bulletproof.”
Building something ambitious, or fixing something that's gone sideways?
Tell us where you are and where you're trying to get to. We'll tell you honestly whether — and how — we can help.