AI Chatbot Development
We build AI chatbots and conversational agents that integrate with your tools, recover gracefully when things break, and ship with an eval harness so quality doesn't silently regress. Customer support, in-product copilots, lead qualification, internal knowledge, voice. From $40,000, 6-8 weeks to production.
6 proven patterns · 6-8 wk typical build · Eval harness from PR #1
Use cases
Production-grade, not demo-grade. Each pattern has a measurable outcome, a known failure mode, and a runbook. Pick the one closest to your workflow; we tune from there.
25-40%
auto-resolution rate
Replaces FAQ articles + tier-1 support. Reads tickets, queries internal systems (orders, accounts, knowledge base), drafts replies, hands off to humans on edge cases. Eval harness from day one to keep regressions out.
+15-35%
feature engagement
Context-aware assistant scoped to user data and product domain. Surfaces relevant docs, drafts outputs, takes light actions. Deeply embedded in your product, not a chat overlay.
30-50%
more qualified appointments
24/7 conversational intake on web + SMS. Captures intent, budget, timeline, and routes to the right human with full context. Replaces static contact forms.
70% reduction
in 'where do I find?' tickets
RAG-powered agent on your wiki, Confluence, Notion, Slack history, and internal docs. Employees ask in natural language, get cited answers. Permission-aware so each user sees only their accessible content.
+25-60%
search-to-purchase rate
Conversational product discovery. Replaces keyword search with intent-aware recommendations, comparison guidance, and cart actions. Handles returns, sizing, fit questions on its own.
Sub-2s
first-token latency
Voice-first interface for support, scheduling, or operations. Streaming response, interruption handling, real-time tool calls. Twilio + ElevenLabs + LiveKit stacks where needed.
Stack
Boring where boring works, opinionated where it matters. We avoid framework churn; the stack stays maintainable after we hand off.
Foundation models
Default: Claude Sonnet 4.6 with prompt caching. Routing tier: Haiku, Gemini Flash. Hard reasoning: Opus, GPT-5. Open-weight (Llama, Mistral, Qwen) when on-prem or cost-bound.
Retrieval / RAG
pgvector or Postgres-native for under 10M docs. Pinecone or Qdrant for scale. Hybrid keyword + semantic on a single index. Citation-first output schema so the agent can never make up sources.
Orchestration
Single-loop ReAct for most chatbots. Plan-and-execute for multi-step workflows. Pydantic AI for typed tool definitions; LangGraph if state machine complexity needs it.
Frontend
Embedded widget (React component) on your site. Slack, MS Teams, Discord adapters. SMS, WhatsApp, Telegram via Twilio. Native iOS / Android via SDK if needed.
Observability + evals
LangSmith or Langfuse for traces. Custom eval harness with golden test set; CI gates merges that drop quality. Cost + latency dashboards from day one.
Integrations
Internal: Postgres, MySQL, Snowflake, BigQuery, S3. SaaS: Salesforce, HubSpot, Intercom, Zendesk, Notion, Confluence, Linear, Jira, GitHub. Custom: any REST/GraphQL/gRPC API your team already exposes.
How we ship
01
Define the workflow the chatbot replaces. Map data sources. Establish the eval set. Pick the model tier. Output: fixed-price proposal with measurable success criteria, plus a throwaway prototype on the riskiest assumption.
02
Core conversation loop, tool integrations, retrieval pipeline, eval harness, observability. Weekly demos. Beta-cohort deploy at week 4. Eval CI gates from PR #1.
03
Staged rollout (1% to 10% to 50% to 100%). Cost + latency baselines. Runbook + on-call handoff. Documentation for your team to extend later.
Pricing
Discovery sprint: $8,000-$15,000 (1-2 weeks). Output: working prototype on the riskiest assumption + fixed-price build proposal. Credited toward the build if you proceed.
Fixed-price build: $40,000-$120,000 typical. Drivers: number of tool integrations, eval-harness rigor, custom UI surface area, compliance requirements (HIPAA, SOC 2, on-prem).
Post-launch retainer: $5,000-$15,000/mo for prompt tuning, eval-harness operations, and managed AI services. Optional. Most customers run their own after the handoff sprint.
Adjacent services
By industry
Frequently asked
An AI agent is software that uses a language model to plan and take multi-step actions toward a goal, calling tools (APIs, databases, other systems) along the way. The minimal pattern: a model + a set of tools + a control loop. Unlike a chatbot — which responds and waits — an agent acts, observes the result, and decides what to do next, often across dozens of steps.
A chatbot turns user input into a response and stops. An agent turns user input into a plan, executes that plan by calling tools, observes the results, and revises until the goal is met or it asks for help. A chatbot answering 'what's my order status' reads from a knowledge base. An agent handling the same query queries the orders API, checks the shipping system, identifies a delay, drafts a refund request, posts it to the ticket queue, and emails the customer.
Generative AI is a capability: producing text, images, code, audio. Agentic AI is an architectural pattern that uses generative AI to drive autonomous, multi-step action with tools. All agentic AI uses generative AI under the hood; not all generative AI is agentic. A summarization endpoint is generative but not agentic. A customer-support agent that reads tickets, looks up orders, and posts replies is both. The agentic pattern is what unlocks measurable business outcomes.
Working prototype: 2 weeks. Production-grade agent (with eval harness, guardrails, observability, and a runbook): 6–10 weeks. The prototype-to-production gap is where most projects fail — the prototype handles the happy path; production has to handle the long tail.
A production AI agent at AISD typically costs $40,000–$150,000 depending on complexity. Drivers: number of integrated systems, evaluation rigor required, compliance overhead, and ongoing operational scope. Prototypes alone are cheaper ($10k–$25k) but rarely worth it without a path to production.
Four predictable failure modes. Tool errors: an API the agent calls is down or returns unexpected data and the agent doesn't recover gracefully. Prompt injection: user-controlled text reaches the agent and overrides its instructions. Cost spirals: an agent that loops without termination conditions burns inference budget. Distribution shift: input patterns change after launch and the agent's prompts no longer match reality. Mitigations: strict tool-call schemas, prompt-injection test suites in CI, cost caps, and weekly eval re-runs.
Three layers of measurement. Offline: a golden test set of 50–500 representative inputs scored automatically (model-graded) and by humans on a sample. Run on every PR. Online: per-call metrics — latency, cost, tool-call success rate, schema-validation pass rate, downstream business outcome. Human-in-loop: weekly review of escalated and low-confidence cases, fed back into the test set.
It depends on workflow shape and team. n8n wins when the agent is mostly orchestrating SaaS tools and the control flow is straightforward — deploys faster, easier for non-engineers to maintain. LangGraph wins when the agent has complex branching, multi-agent coordination, or needs tight Python integration with custom code. From scratch wins for simple, high-volume agents where every layer of abstraction is overhead.