inparlor.
Development

AI Chatbots & AI Agents.

Chatbots, AI agents, and RAG assistants that ship to production, not demos.

By the numbers

  • 3-8 wkper AI integration
  • 30+LLM features shipped
  • 100%evals before launch
What this includes

Deliverables, line by line.

  • Scoping doc that names the one workflow AI will actually improve
  • RAG pipeline over your documents with source-cited answers
  • Chatbot or in-product assistant wired to your data and actions
  • Agent design with defined tools, action boundaries, and approval steps
  • Eval suite that scores accuracy before and after every change
  • Guardrails for prompt injection, PII, and off-topic responses
  • Vector store setup in pgvector or Pinecone
  • Streaming UI built on the Vercel AI SDK
  • Cost and latency monitoring per feature
  • Fallback and human-handoff paths for low-confidence answers, with source code and prompts in your GitHub org
Process

How an engagement runs.

  1. Use-case scoping

    One week. We separate the chatbot and agent ideas worth building from the ones that sound good in a meeting. You leave with a scoped feature, a cost estimate per request, and a definition of done that is measurable.

  2. Data and retrieval

    We ingest your documents, chunk and embed them, and stand up retrieval so answers are grounded in your data, not the model's memory. Every answer can cite its source.

  3. Build and integrate

    We wire the chatbot or agent into your product with streaming responses, the right model per task, and the actions it is allowed to take. You demo it in your own app, not a sandbox.

  4. Evals and guardrails

    We build a test set of real questions with known-good answers and score every change against it. Guardrails catch injection, PII leakage, and out-of-scope requests before launch.

  5. Production rollout

    Feature-flagged launch to a small cohort, with cost and latency dashboards live from day one. We tune prompts, retrieval, and agent behavior against real usage before expanding.

Stack

Tools we run with, by default.

  • OpenAI API
  • Anthropic API
  • Vercel AI SDK
  • LangChain
  • pgvector
  • Pinecone
  • Next.js
  • TypeScript
  • Postgres

We will work in your existing stack when it fits. We swap tools only when the cost of staying is higher than the cost of moving.

Pricing

Transparent, itemized, no day rates.

Custom quote

fixed price, scoped to you

Every engagement is scoped against your spec. Every proposal is itemized: design, build, content, and integrations — so you can sanity-check the math before signing.

Timeline

3 to 8 weeks per integration

For mixed engagements (build + maintain), we bundle the proposal with a single price you can take to the board.

Featured work
E-commerce / DTC

−38%

support tickets in 60 days

DTC brand, e-commerce

High-growth DTC brand drowning in order-status and returns tickets, with a 6-hour first-response average. We integrated an AI support assistant over live order data; tickets dropped 38%, first response became instant, and CSAT improved 12 points.

Read the case study
Pair this with

Most clients run two or three of these together.

Stack thinking: paired engagements share one measurement layer, one accountability ledger, and one quarterly business review. That's where the compounding comes from.

Adjacent reading

Comparisons and cost guides for this engagement.

FAQ

What buyers ask before signing.

Ready to start?

Ready to start with AI Chatbots & AI Agents?

Get a proposal for AI Chatbots & Agents.

Name, email, and one line on what you need. Written scope, timeline, and price in 24–48 hours — no discovery call required to schedule a discovery call.

Response within 24 hours from a real strategist.