Internal AI assistant agency
Build an internal AI assistant for your company
We build AI assistants for your team and customers, grounded in your company knowledge. Hosting and model access are chosen to meet your data requirements.
Who this is for
Three signals that an AI assistant will pay back fast: internal team, customer-facing, or both.
Leaders watching the team re-ask the same questions every week
Onboarding, policy, customer history, product details, past incidents. The answers exist somewhere in Slack, Notion, the CRM or shared drives. Nobody finds them. You need an assistant that does.
Support and sales teams answering the same customer questions every day
Order status, product details, policies, troubleshooting, onboarding steps. Customers ask on your website, WhatsApp, email and chat. You need an assistant that handles tier-1 with citations and routes the judgment calls to humans with full context.
Tech leaders who need data-resident AI on private infrastructure
You can't put company documents or customer data into a public chatbot. You want AI that runs in your environment, uses your own API keys, and leaves a clean audit trail.
What we build
Concrete deliverables, not slideware.
How we work
A four-step engagement designed to ship in weeks, not quarters.
- 130-min call
AI assistant review
We map the daily questions your team and customers ask, where the answers live today, the surfaces (Slack, Teams, web chat, customer portal, voice) and the data residency constraints. You leave with a 1-page recommendation, even if you don't engage us.
- 21 week
Assistant blueprint
We deliver a written blueprint: data sources and access model, RAG architecture, model choice and fallbacks, surfaces, evaluation plan, hosting plan, scope, timeline and fixed quote. You decide whether to proceed before any code is written.
- 32 to 6 weeks
Build & evaluate
We ingest your documents, build the assistant, wire it into the surfaces you need (Slack, Teams, web chat, customer portal, voice), and evaluate against a real question set covering both your team and a representative customer scenario. We deploy to your environment, instrument logging, and hand over documentation.
- 4Ongoing
Operate & extend
We monitor quality, refresh the document corpus, swap to new models as they ship, respond to incidents, and ship 1 to 3 enhancements per sprint. Cancel any time.
What we've shipped
A few examples from the broader portfolio.
AI Personal Agent
Personal AI agent that handles focused tasks on demand: research, drafting, analysis, summarization. Wired to your tools and trained on your workflow.
Read the case study →AI Assistant for team or customer
AI assistant grounded in your knowledge base, deployed as an internal team helper or a customer-facing portal. RAG with citations to source documents, strict access control, swappable model.
Read the case study →Frequently asked questions
What buyers usually ask before engaging.
How long does an AI assistant project take?
Most engagements ship a working assistant in 2 to 4 weeks and reach steady state in 4 to 8 weeks. The first useful version (RAG over a defined doc corpus, in Slack, web chat or a customer portal) lands fast. Voice interfaces, multi-system search and complex tool use sit at the longer end.
Which AI model do you pick (OpenAI, Claude, Gemini, others)?
We compare models on your tasks, using answer quality, latency, cost, tool use and hosting requirements. This can include OpenAI, Claude, Gemini or locally deployed models such as Llama, Mistral and Qwen. Local inference is an option when data must stay on private infrastructure. We design so the underlying model can be replaced.
How do you handle our data and privacy?
We agree which data can leave your environment before choosing the deployment. Local inference keeps model processing on your infrastructure; external model APIs receive the content needed for each request. We configure access controls and audit logs, and review the provider's retention and training terms with you. We do not train models on your data.
How do you price AI assistant projects?
Fixed-price for the build phase based on the blueprint scope, then a monthly retainer for hosting, monitoring and continuous extension. Final pricing depends on the data sources to index, the LLM models and SLAs, the surfaces where the assistant lives, and whether you need inference on private infrastructure. LLM API costs are passed through at provider rates. We share a precise number after the 30-minute review, with no obligation to engage further.
What does ongoing maintenance look like?
We monitor assistant quality, evaluate new models as they ship, refresh the document corpus, respond to incidents, run weekly backups, and ship 1 to 3 enhancements per sprint. You get a shared Slack channel and a monthly written report.
How do I get started?
Book a 30-min AI assistant review. You'll leave with a 1-page recommendation tailored to your stack, even if you don't engage us. If we're a fit, the blueprint phase begins the following week.
Stop watching the same questions queue up.
Let's build an assistant that answers from your company knowledge, with access controls, audit trails and hosting suited to your data requirements.
Explore other services
What else we build for SMEs.