Architecture review
We document the target architecture and trade-offs before any code is written.
AI
LLM App / Agent Build.
A production LLM application or agent with the unglamorous parts done right — retrieval, tool use, evaluations, and observability.
Production-ready LLM app or agent — RAG, tools, evals — built on Claude, OpenAI, Gemini, or open models.
What you get.
Every deliverable below is included in the scoped engagement — no upsell at handoff.
Production deploymentDeployed to your infrastructure with a real domain, real auth, and real users — not a notebook.
Retrieval pipelineVector store, chunking strategy, hybrid search, and reranking — tuned to your data, not the LangChain defaults.
Tool useFunction calling and tool orchestration with proper error handling and a fallback path for every external call.
Evaluation suiteA set of evaluations that catch regressions before they hit production — code-based, LLM-judged, or both.
ObservabilityTracing, latency, cost, and error monitoring wired in from day one — usually LangSmith, Helicone, or Arize.
Operational handoffRunbook, alerts, and a session with your on-call team so you can own it on day one.
How it works.
The same four-step flow we use across every engagement, scoped to this gig.
Build
Iterative weekly drops with a working preview environment from week one.
Evaluate
We don't ship without an evals pass that catches the regressions you'd care about.
Handoff
Deployment, runbook, and a walkthrough so your team owns it from day one.
Tools we use.
The stack we default to for llm application and agent development work. Always open to fitting yours.
Claude
OpenAI
Gemini
LangChain
LlamaIndex
Pinecone
pgvector
LangSmith
Why work with us on this.
Three reasons clients pick Hepha Works for llm application and agent development.
Senior practitioners only
The person who scopes the work is the person who delivers it. No invisible subcontractors, no junior handoffs.
Written scope, fixed price
You see a written scope and a number before any work starts. No timesheet surprises, no scope-creep arguments.
Honest read on outcomes
We won't say it'll work if we don't think it will. If the gig isn't right for your situation, we'll tell you that on the call.
Frequently asked.
The questions we get most before kicking off llm application and agent development engagements.
Do you work with open-source models?
Yes — Llama, Mistral, and Qwen all supported. We'll recommend based on your latency, privacy, and cost requirements.
Can you host it on our infrastructure?
Yes — AWS, GCP, Azure, or on-prem. We'll match your existing stack rather than push our own.
How do you handle evaluation?
A mix of deterministic code-based checks and LLM-as-judge for subjective quality. Custom eval sets are built per project.
What about cost optimization?
We model expected per-request cost during the architecture phase, and tune model choice, caching, and routing for ongoing spend.
Can you migrate from a prototype someone else built?
Yes — we'll audit the prototype first and tell you what's worth keeping before quoting the rebuild.
Related engagements.
Other gigs that pair well with LLM App / Agent Build.
Ready to start?
Send a brief and we'll come back with a written scope and a number within 1–2 business days.