AI chatbots that actually understand your customers.
For SaaS teams, e-commerce brands, and support-heavy businesses. Production-ready in 4 weeks. Grounded in your data. Measured against real resolution — not vanity metrics.
A chatbot isn’t a feature. It’s a conversation policy, executed in code.
Most chatbots ship without an answer to the only question that matters: what is this thing allowed to say, and how do we know it stayed inside that line yesterday? We build chatbots backwards from that question. Conversation policy first, retrieval second, prompt last.
Every chatbot we deploy ships with an evaluation harness — a written set of behaviors we measure on every change. Refusals when out-of-scope. Citations when answering from your knowledge base. Escalation to a human when confidence drops. Latency budgets per route. The unglamorous work that turns a demo into a system you can rely on at 3am.
We work in OpenAI, Anthropic Claude, Google, and open-source. We pick based on the workload, your privacy posture, and the cost-per-conversation math — not on what’s trending on Twitter this month.
What’s included.
Knowledge integration (RAG)
Hybrid retrieval over your docs, tickets, product copy, and policies — with citations, freshness controls, and access-aware permissions.
Multi-channel deployment
Web widget, WhatsApp, Slack, Teams, mobile, and voice. Same brain, channel-appropriate behavior.
Tooling & integrations
Read-and-write integrations with Salesforce, HubSpot, Zendesk, Intercom, Stripe, Shopify, Notion, Linear, and your own APIs.
Human escalation flows
Hand-off to your team when confidence drops, with full conversation context. No re-asking the customer for their order number.
Evaluations & guardrails
200–2,000 ground-truth examples covering scope, tone, refusals, and citations. Regression tests on every prompt change.
Analytics dashboard
Resolution rate, escalation rate, deflection cost savings, top intents, drift over time. Looker Studio or your warehouse.
Monthly tuning
Weekly prompt and retrieval improvements. Monthly model evaluation. Quarterly model upgrades. AI degrades silently — we keep watch.
Where this works best.
Internal employee helpdesk
IT, HR, finance — answers grounded in your policies, with audit logs and access controls.
How we build it.
Audit
We review your existing support flows, ticket data, knowledge base, and CRM. We identify the 20% of intents that drive 80% of volume — the ones that pay for the entire build.
Design
We write the conversation policy: scope, tone, refusal behavior, escalation rules, citation format. Reviewed and signed off before we write a prompt.
Build
We integrate the LLM, build the retrieval pipeline, wire in tools, and stand up the eval harness. Staged behind a feature flag from day one.
Pilot
We deploy to 5–10% of conversations and watch resolution, escalation, and customer-effort metrics for two weeks before we scale.
Scale & tune
We expand traffic, monitor cost-per-conversation, and tune weekly. Monthly business reviews against the metrics that matter to you.
Tools we use.
Engagement models.
Pilot
from $18k
A focused 4-week deployment for one channel and one use case — typically support deflection.
- 1 LLM, 1 channel, 1 use case
- Up to 5 integrations
- 200-example eval harness
- 30 days of post-launch tuning
Production
from $48k
A multi-channel chatbot embedded in your product and stack, with full analytics and escalation flows.
- Multi-channel (web + 2 more)
- Up to 15 integrations
- 1,000-example eval harness
- 90 days of post-launch tuning
Enterprise
custom
Private deployment, fine-tuning, multi-tenant, regulated industries, and custom evaluation pipelines.
- Private / VPC deployment
- Custom model fine-tuning
- SOC 2 / HIPAA-aware design
- Ongoing retainer for tuning
Frequently asked.
7 questions answered. Still have one? Reach out.
It depends on the workload. For high-stakes reasoning, complex policy following, or multi-step tool use, Claude is our default. For high-volume classification and chat, GPT-4.1-mini or Llama 3.3 70B are usually cheaper. We benchmark on your data before recommending.