Frequently Asked Questions

AI systems that run business operations. That includes agentic AI agents that reason and act across your tools, voice agents handling calls around the clock, workflow automation on n8n and Make, custom RAG pipelines built on your data, and self hosted AI infrastructure for teams that can't send data to third parties. We also deploy and harden OpenClaw for businesses that want local AI infrastructure.

It varies. Solo founders automating their first workflow, mid-market teams (50 to 500 people) replacing manual processes they've outgrown, enterprise teams with compliance requirements that rule out most cloud AI tools. Healthcare, legal, recruiting, call centers, real estate, e-commerce, finance, marketing agencies. The common thread is that they have an operational bottleneck and want it gone.

No. A solo operator running a service business is as valid a client as an enterprise IT team. The scoping and price change; a single workflow automation might cost $5,000, a multi-agent enterprise system might run $100,000+. But the engineering approach doesn't.

Healthcare (HIPAA-compliant self hosted deployments), legal (intake and document pipelines), recruiting and staffing (candidate screening, ATS automation), call centers (inbound voice AI at scale), real estate (lead qualification voice agents), finance and fintech (KYC/AML pipelines, onboarding automation), e-commerce (support automation, order handling), marketing agencies (reporting, CRM sync, white-label AI). Regulated and non-regulated.

We build. We don't configure platforms and hand you a login. Every system is engineered for your specific workflows. We work in regulated environments most agencies won't touch: HIPAA, GDPR, KYC/AML, air-gapped deployments. And you own everything. Code, models, configs, docs. No platform lock-in, no seat fees back to us.

Single workflow automations typically run $5,000 to $15,000. Custom AI agents with a defined scope are $15,000 to $40,000. Multi-agent enterprise systems run $50,000 to $150,000+. Voice agent deployments land between $8,000 and $30,000 depending on call volume and integrations. Compliance environments carry additional infrastructure costs on top. Every engagement starts with a free audit call so we can scope it properly. We don't quote blind.

It's free. In 15 minutes (or async if you prefer), we map your highest-friction workflows, identify the two or three automation wins with the clearest ROI, and tell you honestly whether building something custom or using an existing platform tool makes more sense. You walk away with a prioritized roadmap. No obligation.

Yes. Retainers run $1,500 to $8,000/month depending on scope, covering monitoring, performance tuning, API and model updates, and feature iteration. If you're running complex agentic systems or voice agents with real call volume, you'll want one. LLM APIs change, workflows evolve, agents drift. Somebody needs to be watching.

That's how we prefer to work. Pick one painful operational problem, automate it, make sure it runs reliably. Then we expand from there: more agents, more connected systems, a voice layer if it makes sense. Everything we build can grow with you.

With the audit call. After that we move into discovery: workflow mapping, stack assessment, integration requirements. Then architecture, then build. Most projects spend two to four weeks in discovery and scoping before any code gets written. We share an explicit scope of work before you commit to anything.

Single workflow automations: 2 to 4 weeks. Custom AI agents with defined integrations: 4 to 8 weeks. Multi-agent systems or voice agent deployments: 8 to 16 weeks. Compliance environment builds add 2 to 4 weeks for infrastructure and security review. The biggest variable is how quickly your team can give us access to existing systems and sign off on scope.

Yes. All of it. Code, configurations, models, documentation. It all transfers to you at delivery. No licensing fees, no platform dependency on us. If you bring in another team to maintain it later, they'll have everything they need.

Not much, but not zero. We need access to your systems, two or three sessions with whoever knows the workflow best (30 to 60 minutes each), and someone available for a weekly check-in during the build. After launch, most clients run things without us unless they're on a retainer.

For agentic AI: Claude Agent SDK, LangChain, LangGraph, MCP servers for tool access. For voice agents: Retell AI, Vapi, Bland AI, chosen based on call volume, latency, and what you need to integrate with. Workflow automation runs on n8n (self hosted or cloud) and Make. RAG and knowledge systems use Qdrant, Weaviate, or pgvector depending on scale. Infrastructure is Docker on self hosted Linux or cloud providers.

Depends on the problem. For standard automation workflows, n8n or Make is faster and easier to maintain. For multi-step agents with custom logic, memory, and tool use, we write code. We pick whatever will serve your operations best over time, not whatever demos the fastest.

It's a phone-capable AI system that answers calls, holds a conversation, collects information, and takes action. That might mean booking appointments, qualifying leads, routing calls, or logging data to your CRM. Under the hood, it runs on a voice platform (Retell AI, Vapi) connected to an LLM and integrated with your calendar or scheduling system. Latency is typically under 500ms. Most callers can't tell it apart from a human receptionist on routine calls.

A chatbot answers questions. An agentic AI system acts on them. An agent can query your CRM, draft a document, send an email, check a database, and route an approval in sequence without someone triggering each step manually. Chatbots are mostly single-turn. Agents close loops across multi-step workflows that used to require a person at every stage.

Yes. We've done it. The setup uses self hosted infrastructure so patient data never touches a third-party cloud. That means a self hosted LLM (Llama, Mistral, or similar via Ollama), a self hosted workflow layer (n8n on your server), and appropriate access controls. These builds take longer and cost more, but they work. Book an audit call and we'll walk through your specific requirements.

It means the language model, the workflow engine, and the vector database all run on your infrastructure instead of someone else's cloud. Your data stays in your environment. If you're in healthcare, legal, finance, or any industry with data residency rules, that's not optional. We handle the deployment on your servers or private cloud.

No. Your data is used for inference only: answering questions, making decisions. Not for training or model updates. When we use cloud LLM APIs, we configure them to opt out of data training by default. If even API calls create too much exposure, self hosted deployments remove that risk entirely.

Every system goes through a monitored period after launch, typically two weeks. We watch for errors, unexpected outputs, or integration issues and fix them at no additional cost. After that, you can run it yourself or move to a retainer.

It happens. LLM APIs update, third-party services change schemas, workflows hit edge cases. If you're on a retainer, we handle it. If not, we offer a discounted hourly rate for post-launch fixes. Either way, we document every integration point so your team can diagnose problems on their own if they want to.

Yes. Every delivery includes documentation on how the system works, how to update its configuration, and how to read its outputs. If your team wants hands-on training sessions, we do those too. The point is that you should be able to run it without us.