Service
Off-the-shelf chatbots don't know your product, your pricing, or the nuance behind how your team answers hard questions. We build AI assistants that do.
Midus designs, trains, and deploys AI assistants grounded in your own data — documentation, tickets, playbooks, product copy, customer history. The result is an assistant that answers like your best internal expert, not a generic language model guessing at your industry.
Who this is for
Your agents spend 40% of their day answering the same ten questions. A documented knowledge base exists, but customers don't read it. Tickets queue up, responses get shorter, quality drops.
Pre-sales engineers burn hours digging through specs, compatibility matrices, and past RFPs to answer one prospect question. Deals slow down. The answer exists; finding it is the problem.
Policies live in one wiki, procedures in another, contracts in a third system, and tribal knowledge in Slack threads no one searches. New hires take months to ramp. Senior staff spend their day being a human search engine.
What we build
Every assistant is custom. The components below are what we typically ship together; the mix depends on where your content lives and where the assistant needs to show up.
Your assistant pulls from your own content — PDFs, wikis, Notion, Confluence, ticket history, product specs. Answers cite the source so your team can verify. Hallucinations drop sharply once the model has real context to work with.
Generic AI sounds generic. We tune on your existing writing — support replies, sales emails, product copy — so the assistant sounds like you, not like a stock model with a corporate HR filter.
Assistants that don't just answer, they do. Pull a customer record, create a ticket, check inventory, draft a response for a human to approve. We wire the assistant into the tools your team already uses.
Web widget, Slack bot, Microsoft Teams app, email handler, or API endpoint for your existing product. The assistant shows up where your users already are — no "install this new app" friction.
Explicit topics the assistant will decline. Confidence thresholds that escalate to a human. PII redaction on inputs and outputs. Audit logs so you can see what was asked, what was answered, and when.
Every conversation becomes training signal. Thumbs-up/down, edit-before-send, and a review queue for the hard cases. The assistant gets measurably better over time rather than plateauing on day one.
Outcomes
Example scenarios below reflect the range we typically see. Your numbers will depend on volume, content quality, and how deeply the assistant integrates with your tools.
Support deflection
For a SaaS product with a mature help center, a grounded assistant typically deflects the repetitive "how do I reset my password / where do I find X" load, freeing agents for the genuinely complex tickets.
Example scenario.
Sales response time
An assistant with access to product specs, past RFPs, and compatibility data can answer 70–80% of prospect questions immediately, letting reps focus on deal-shaping instead of fact-finding.
Example scenario.
Onboarding ramp
A company assistant that knows your policies, processes, acronyms, and tooling cuts the "who do I ask" phase dramatically. Senior staff stop being the help desk.
Example scenario.
How we work
We map the use cases that matter, inventory your content, and identify gaps. Output is a written scope: what the assistant will and won't do, what sources it will ground on, which channels it ships to, what "done" looks like.
We stand up retrieval, pick models, wire up tool use, and tune voice. You see working prototypes within the first two weeks, not a slideware update.
Internal pilot first, then controlled rollout. We instrument everything, review the first few thousand conversations with you, and tune based on what actually happens — not what we assumed would happen.
FAQ
That depends on which model you're comfortable with. We can run entirely on self-hosted open models (Llama, Qwen, Mistral family) on your own GPUs — nothing leaves your network. We can also use commercial APIs (Anthropic, OpenAI) with zero-retention agreements. The right choice depends on your data sensitivity, regulatory posture, and cost tolerance. We walk through the trade-offs in scoping.
Three layers. First, retrieval — answers are grounded in your actual content with citations. Second, confidence thresholds — below a certain score the assistant says "I don't know, let me escalate" instead of guessing. Third, an evaluation harness we run continuously against known Q&A pairs so regressions show up before your users do.
Hosted commercial models: typically a few cents per conversation, scaling with usage. Self-hosted open models: fixed GPU cost, predictable. For most mid-market deployments we see $400–$2,000/month in inference cost once traffic settles. We spec realistic numbers during scoping.
Typical range is 4–8 weeks from kickoff to live pilot. Tight-scope assistants (single use case, clean content, one channel) land in four. Broader deployments (multi-source retrieval, tool integrations, multiple channels) land closer to eight. We don't drag projects.
That's the normal starting point. Part of the scope phase is auditing what you have, flagging what's stale, and deciding what's worth cleaning up first. We don't block the project on "first, reorganize your wiki" — we ship with imperfect content and improve it in flight.
Yes, by default. Assistants need ongoing tuning as your content, product, and user expectations change. We offer month-to-month support plans that cover monitoring, tuning, and feature additions. You can leave any time with full knowledge transfer.
Related services
Tell us the use case, what content you have, and where it needs to show up. We'll come back with a realistic plan, cost range, and timeline — typically within two business days.