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Services / build / automation
RAG-as-a-Service
Your docs, searchable by an agent that cites every answer.A fully managed retrieval-augmented agent over your knowledge base. We index your docs, build the retrieval pipeline, evaluate citation accuracy, and operate it month over month. Your team asks questions in Slack, the customer-facing widget, or your app — the agent answers with citations or admits it does not know.
service graph
Surface ⇄ System
- price
- from $1,800
- timeline
- 2–3 weeks setup
- cadence
- one-time
price
from $1,800
timeline
2–3 weeks setup
cadence
one-time
scope
One-time / fixed scope
PineconepgvectorOpenAIAnthropicLangChainRe-rankers
00// matrix position
Where this fits in the services matrix.
Every service page now names the buyer state, the commercial shape, and the next route. That keeps the catalog navigable instead of feeling like disconnected offers.
01 · best fit
Build automation with a fixed scope and written handoff.
02 · commercial shape
from $1,800 · 2–3 weeks setup · One-time / fixed scope
03 · route logic
Use the diagnostic or book a call to confirm fit before scope is written.
04 · decide
Not sure this is the right service? Run the route finder and get the matching path.
00B// system flow
The offer is a route, not a loose task list.
This diagram gives every service page a concrete operating model: intake, system design, implementation, proof, and handoff.
service operating path
Surface ⇄ System
RAG-as-a-Service flow
The diagram is intentionally simplified: it shows the buying logic and operating path, not a decorative fantasy architecture.
price
from $1,800
timeline
2–3 weeks setup
cadence
one-time
01// what you walk away with
The outcome, not just the output.
- 01Production RAG agent over your knowledge base
- 02Citation-grounded answers (every claim links back to source)
- 03Eval suite that catches retrieval drift
- 04Slack / widget / API endpoints ready to use
- 05Honest "I do not know" behavior on out-of-scope questions
02// scope
Concrete artifacts you keep — and what we leave out.
Working code, written docs, dashboards your team owns. We also list what this engagement deliberately does not cover, so scope is honest before you click.
// deliverables
- Indexed corpus (up to 50k pages) with chunking strategy tuned to content
- Retrieval pipeline with re-ranker + hybrid search (vector + BM25)
- Citation-formatted response template with source links
- Eval harness: 50+ Q&A pairs scored on accuracy + citation correctness
- Slack bot + JS widget + REST endpoint
- 30 days of post-launch monitoring + tuning
// not included
- Annotation labor at scale (we set up the loop; ongoing labeling is on you)
- Multi-tenant authorization layer (separate engagement)
- Translation of source docs (English-only by default)
03// methodology
How the engagement actually runs.
- 1Week 1
Index + retrieval
Ingest corpus, pick chunking strategy, build hybrid retrieval with re-ranker.
Indexed corpusRetrieval pipelineChunking spec - 2Week 2
Agent + evals
Wire LLM answer-generation with strict citation format. Build 50+ eval cases. Tune until accuracy clears threshold.
Agent promptEval suiteAccuracy report - 3Week 3
Surfaces + handoff
Ship Slack bot, JS widget, and REST endpoint. 30-day post-launch tuning included.
Slack botJS widgetAPI endpointRunbook
// track record
Receipts, not promises.
- 85–95%
- Citation accuracy
- on eval suite
- <2s
- Median response
- p50 latency
- 50k
- Pages indexed
- base scope
04// questions
Common questions.
01How big can the corpus be?
We index up to 50k pages in the base scope. Larger corpuses (100k+) add 1 week and are priced separately.
02What is the ongoing cost?
Vector storage runs $20–$200/mo depending on volume. LLM inference depends on traffic — typical SMB: $40–$400/mo. You pay these directly (BYOK).
03How accurate is it really?
On the eval suite we ship, expect 85–95% citation accuracy out of the gate. We tune to your domain in the 30-day post-launch window.
// engage
Ready to start RAG-as-a-Service?
A 30-minute call to confirm fit, scope, and timeline. No pressure, no slides.
automation system
From offer to operating system.
RAG-as-a-Service is presented as a real engagement, not a generic service page: the surface, backend shape, delivery artifacts, and conversion path are all visible before the first call.
Scope RAG-as-a-Serviceautomation system
Surface ⇄ System
- price
- from $1,800
- timeline
- 2–3 weeks setup
- tier
- B
price
from $1,800
timeline
2–3 weeks setup
tier
B
Living architecture
Scope ⇄ Ship
The page now exposes how the engagement moves from buyer pain to production artifact, then into measurement and next-step routing.
Scope RAG-as-a-Service- 01Index + retrievalIngest corpus, pick chunking strategy, build hybrid retrieval with re-ranker.
- 02Agent + evalsWire LLM answer-generation with strict citation format. Build 50+ eval cases. Tune until accuracy clears threshold.
- 03Surfaces + handoffShip Slack bot, JS widget, and REST endpoint. 30-day post-launch tuning included.
Conversion path
Surface ⇄ System
01
Diagnose
Confirm the real automation constraint, current surface, and business goal before writing code.
02
Design the system
Turn the offer into screens, data, workflows, ownership boundaries, and a measurable delivery plan.
03
Ship the artifact
Deliver RAG-as-a-Service as working code, docs, dashboards, or launch assets your team can actually use.
04
Route the next move
Decide whether the work becomes a one-time delivery, a care plan, or a larger product build.
Proof assets
Real only
Asset slot
Service proof visual
Add a real screenshot, deliverable preview, or dashboard capture from a shipped engagement when approved.
Verified asset
Founder/operator photo
Real founder photo reinforcing principal-led delivery.
Asset slot
Client quote or logo
Add only permissioned testimonials or logos tied to this service category.