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.
price
from $9,500
timeline
4–6 weeks
cadence
one-time
scope
One-time / fixed scope
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.
Build automation with a fixed scope and written handoff.
from $9,500 · 4–6 weeks · One-time / fixed scope
Use the diagnostic or book a call to confirm fit before scope is written.
Not sure this is the right service? Run the route finder and get the matching path.
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
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
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.
- 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
- Building the front-end UI (separate scope)
- Hosting your corpus indefinitely (we deploy to your infra)
- Custom model training
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
Receipts, not promises.
- 4–6 wk
- Production timeline
- ≥ 0.85
- Typical recall@10
- on tuned corpora
- < $0.01
- Median cost / query
- after optimization
Common questions.
01How big a corpus can you handle?
02Which vector DB do you use?
03Do you handle sources beyond text?
04What does success look like?
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 Engineeringprice
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.