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Services / build / automation
RAG Systems Engineering
Productionize the prototype your team has been demoing for six months.Most RAG systems are demos pretending to be products. This engagement makes them real: chunking strategy that respects the corpus, retrieval evals you can actually trust, citation accuracy you can defend, and observability so you know when it breaks. Fixed scope, fixed price, deployable.
service graph
Surface ⇄ System
- price
- from $9,500
- timeline
- 4–6 weeks
- cadence
- one-time
price
from $9,500
timeline
4–6 weeks
cadence
one-time
scope
One-time / fixed scope
PineconepgvectorOpenAIAnthropicLangChainLlamaIndexCohere
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 $9,500 · 4–6 weeks · 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// retrieval system
Bespoke architecture for RAG Engineering.
RAG work is only valuable when the answers can be trusted, evaluated, and improved as the underlying knowledge changes.
retrieval system
Surface ⇄ System
RAG reliability loop
The diagram is intentionally simplified: it shows the buying logic and operating path, not a decorative fantasy architecture.
price
from $9,500
timeline
4–6 weeks
failure mode
measured
outputs
cited
01// what you walk away with
The outcome, not just the output.
- 01Chunking + embedding pipeline tuned to your corpus
- 02Retrieval evals running on every change
- 03Citation accuracy measured and reported
- 04Cost ceilings, rate limits, and fallback paths
- 05Deployed to production with monitoring
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
- Ingestion pipeline (with re-indexing strategy)
- Retrieval eval harness — recall@k, MRR, faithfulness, citation accuracy
- Reranker + hybrid search where it earns its cost
- Observability: query logs, eval drift, cost per query, p95 latency
- Production deployment to your cloud (or ours)
- 4 weeks of post-launch tuning included
// not included
- Building the front-end UI (separate scope)
- Hosting your corpus indefinitely (we deploy to your infra)
- Custom model training
03// methodology
How the engagement actually runs.
- 1Week 1
Corpus + query analysis
Sample your corpus, profile query patterns, agree on success metrics. Pick vector DB + embedding model + reranker stack.
Corpus profileQuery taxonomyStack recommendation memo - 2Week 2–3
Pipeline + evals
Build ingestion pipeline, chunking strategy, embedding store. Stand up retrieval eval harness against a golden set.
Ingestion pipelineEval harnessInitial benchmarks - 3Week 4
Reranking + hybrid + cost
Add a reranker only where it pays for itself. Tune hybrid (BM25 + vector). Profile cost per query and add ceilings.
Reranker integrationCost profileRate-limit + fallback config - 4Week 5–6
Production + observability
Deploy to your infra, wire observability, document failure modes. Hand off with runbook and 4 weeks of tuning.
Production deploymentObservability stackRunbookTuning sprint plan
// track record
Receipts, not promises.
- 4–6 wk
- Production timeline
- ≥ 0.85
- Recall@10 target
- agreed before kickoff
- < $0.01
- Cost / query target
- after optimization
04// questions
Common questions.
01How big a corpus can you handle?
We have shipped systems from 10k to 10M chunks. Above that, we scope a longer engagement with sharding strategy.
02Which vector DB do you use?
We start neutral. After looking at your corpus + query patterns + cost ceiling, we recommend Pinecone, pgvector, Weaviate, or Turbopuffer with a written rationale.
03Do you handle sources beyond text?
Text + structured data + tables yes. Images and audio require a longer engagement and are scoped separately.
04What does success look like?
A measurable retrieval quality target (e.g., recall@10 ≥ 0.85), agreed before kickoff, and a CI gate that prevents regressions.
// engage
Ready to start RAG Engineering?
A 30-minute call to confirm fit, scope, and timeline. No pressure, no slides.
automation system
From offer to operating system.
RAG Systems Engineering 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 Engineeringautomation system
Surface ⇄ System
- price
- from $9,500
- timeline
- 4–6 weeks
- tier
- A
price
from $9,500
timeline
4–6 weeks
tier
A
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 Engineering- 01Corpus + query analysisSample your corpus, profile query patterns, agree on success metrics. Pick vector DB + embedding model + reranker stack.
- 02Pipeline + evalsBuild ingestion pipeline, chunking strategy, embedding store. Stand up retrieval eval harness against a golden set.
- 03Reranking + hybrid + costAdd a reranker only where it pays for itself. Tune hybrid (BM25 + vector). Profile cost per query and add ceilings.
- 04Production + observabilityDeploy to your infra, wire observability, document failure modes. Hand off with runbook and 4 weeks of tuning.
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 Engineering 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.