AGENTS01
Conversational & RAG agents
Production assistants grounded in your own data, with retrieval, tool use, and guardrails that hold up under real traffic.
Grounded answers with citations
AI-native software agency
We build production AI, end to end.
From discovery to deploy, we ship grounded agents, automations, and copilots with evals that prove they work.
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scope.summary
RAG support assistant grounded in your docs, with a tool layer and an offline eval suite._
timeline
8-12 wk
team
3-4
evals
live
estimated range
$84,000 – $142,000
Directional ballpark, generated live. Refined on a scoping call.
What we build
AI work, demonstrated
Six capabilities we ship into production. Each one comes with the evals, guardrails, and observability that keep it honest after launch.
AUTOMATION02
Agentic workflow automation
Pipelines that read, decide, and act across your internal tools, with human approval gates where they matter.
Hours of manual work removed per week
DOC INTEL03
Document intelligence
Extraction, classification, and summarization over messy PDFs, contracts, and scans, validated against a real eval set.
Structured data from unstructured docs
VOICE04
Realtime voice agents
Low-latency voice for support, scheduling, and intake that sounds human and routes cleanly to a person when needed.
Sub-second turn latency
COPILOTS05
Internal copilots
Domain copilots wired into your stack with tool use, permissions, and an audit trail your security team will sign off on.
Wired into the tools you already use
EVALS06
Evals & observability
Offline eval suites and live tracing so model quality is a number you watch, not a vibe you hope for.
Quality measured, not guessed
Selected work
One engagement, described in full
Client work is anonymized by policy, so what follows is the system rather than the logo. No metrics are quoted, because none were measured. What is claimed here is what was built.
A hydroponics-and-e-commerce client
Four connected systems, from cited plant science to fulfilment
Cited growing facts feed a publishing engine, the publishing engine and the sales systems all meet at the online store, and an operations layer decides what can actually ship. Each system is built to stop rather than guess.
Read the full case studySolid lines are wired and running today. Dashed lines are built or available and deliberately not switched on. That convention holds across every diagram we draw.
- 01Running
Hydroponics knowledge base
Reads the open scientific and horticultural literature and turns it into structured facts, each carrying a citation back to the exact sentence it came from. When it does not know something, it says so.
- 02Running
SEO and GEO publishing
Writes content optimised for both search engines and AI answer engines, and refuses to publish anything it cannot cite. Every claim is re-checked against the live knowledge base before an article is created.
- 03Running
CRM and the voice sales agent
The agent phones leads, holds a real spoken conversation, qualifies them against a fixed questionnaire, and writes structured sales data straight into the customer record. Every outbound send crosses one compliance layer.
- 04Work in progress
ERP
Pulls the online store and the overseas warehouses into one ledger so stock, orders, fulfilment and cost can be read against each other. Built read-first: every mutating call sits behind a safety gate that is shut.
Four systems, one house style.
These four rules are not specific to hydroponics. They are how we build anything that has to be right rather than merely fast.
Gate first.
The safety boundary is architecture, not a setting. A permission check that runs before the network call. A flag an automated actor cannot flip. A mutating method that has to ask before it acts.
Cite, or do not say it.
Evidence is a precondition, not a review step. A fact whose quote is not in the source is rejected at ingest. An article whose citations do not resolve is never created.
Fail loudly.
When grounding cannot be established the run stops and records which step failed and why. Nothing degrades quietly to a worse answer, and no fallback drafts from the unfiltered set.
Agents propose, people approve.
Where confidence is low the work goes to a queue for a person: ambiguous identity matches, low-confidence support answers, cross-sell campaigns, and every article before it goes live.
Start here
Get an instant project estimate
Tell us what you want to build. Our estimator generates a live, structured scope and cost range in seconds, then we refine it together on a short call.
- 01
Describe the project
A sentence or two on the problem, users, and data is enough to start.
- 02
Get a live estimate
An AI-generated scope, deliverables, timeline, and cost range, in seconds.
- 03
Refine on a call
We turn the ballpark into a firm proposal and a plan to ship.
Prefer to talk first? Email hello@wispgard.com and we will set up a scoping call.