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RAG Deployment

RAG Deployment

FISTA Solutions takes retrieval systems from prototype to production: ingestion pipelines that keep content current, chunking and retrieval tuned against real questions, permission-aware search, citation-backed answers, evaluation that measures retrieval and generation separately, and cost that scales sensibly.

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150+
projects delivered
50+
companies served
99.9%
verified uptime
47%
efficiency gains
12+
countries reached

What we build

What does RAG deployment include?

RAG deployments cover source ingestion with change detection, chunking and embedding strategy, hybrid retrieval with reranking, permission enforcement at query time, citation-backed generation, separate evaluation of retrieval and generation, and freshness and cost monitoring.

  1. 01

    Ingestion pipeline

    Source connectors with change detection and reindexing, so answers reflect current content.

    Ingest
  2. 02

    Chunking and indexing

    Chunking strategy tuned to your document structure, with metadata preserved for filtering.

    Index
  3. 03

    Hybrid retrieval

    Keyword and semantic retrieval with reranking, tuned against real user questions.

    Retrieval
  4. 04

    Permission enforcement

    Access controls applied at query time against the asker, never filtered after generation.

    Security
  5. 05

    Evaluation

    Retrieval and generation measured separately, because a good answer from bad context is luck.

    Quality

Requirements

Which requirements shape RAG deployment?

Production RAG is judged on whether answers are correct, current, and permitted. Requirements cover retrieval quality measured independently, freshness, permission enforcement at query time, citation discipline, and cost that does not grow unbounded with corpus size.

RAG Deployment: requirements and how FISTA Solutions builds to them
RequirementWhy it mattersHow FISTA implements it
Retrieval qualityMost bad answers are retrieval failures.Retrieval evaluated separately with recall metrics against labeled question-document pairs.
FreshnessStale indexes produce confidently wrong answers.Change detection and incremental reindexing with freshness indicators surfaced in answers.
Permission enforcementRetrieval can bypass access controls.Permissions carried into the index and enforced at query time against the asker's identity.
Citation disciplineUnverifiable answers are not trusted.Every claim cited to source passage and version, with abstention when retrieval confidence is low.
Cost with scaleEmbedding and storage grow with the corpus.Embedding cost, storage, and query cost modeled against corpus growth, with retention and tiering.

Where AI fits

How should you sequence RAG deployment?

Move RAG to production by fixing retrieval first: build the labeled question set, measure retrieval independently, enforce permissions from day one, then tune generation and monitor freshness and cost.

  1. 01

    1. Build the question set

    Real user questions with the documents that should answer them, which is the retrieval benchmark.

  2. 02

    2. Measure retrieval alone

    Recall and precision on that set before generation quality is considered at all.

  3. 03

    3. Enforce permissions early

    Access control carried into the index from the start; retrofitting it is painful and risky.

  4. 04

    4. Tune generation

    Citation discipline, abstention behavior, and answer format once retrieval is reliable.

  5. 05

    5. Monitor freshness and cost

    Index staleness, query cost, and unanswered questions tracked continuously.

Cost and timeline

What does RAG deployment cost, and how long does it take?

Cost is driven by corpus size, update frequency, and query volume; timeline by source access and permission modeling. FISTA does not quote blind: the scoping call returns a retrieval design, an evaluation plan, and a cost model.

Corpus size drives embedding and storage cost, while query volume drives inference cost. Both are modeled during design so a growing corpus does not quietly become the largest line in your AI budget.

Permission modeling is the hidden effort in enterprise RAG. Carrying access controls into the index correctly is most of the work in organizations with complex permission structures, and it cannot be skipped.

Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.

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Delivery

How does FISTA deliver an AI deployment?

FISTA deploys AI in four phases: an assessment that inventories workloads, data boundaries, and constraints and produces a target architecture; a platform build with networking, identity, secrets, and observability as code; a migration with evaluation gates and shadow traffic; and a production cutover with dashboards, budgets, runbooks, and rollback.

  1. 1

    Assess and target

    Workload inventory, data classification, latency and volume profile, compliance constraints, and a target architecture with cost model.

    Output

    Target architecture, cost model

  2. 2

    Build the platform

    Networking, identity, key management, model endpoints, gateway, tracing, and evaluation pipeline delivered as infrastructure-as-code.

    Output

    Platform as code, control matrix

  3. 3

    Migrate with gates

    Move applications behind the gateway, run evaluation and shadow traffic, and tune routing, caching, and capacity.

    Output

    Eval reports, shadow results

  4. 4

    Cut over and operate

    Graduated production rollout, dashboards for quality, latency, and cost, runbooks, on-call, and a change process with rollback.

    Output

    Production platform with SLOs

Why FISTA

Why choose FISTA Solutions for RAG deployment?

FISTA deploys RAG with retrieval measured independently, permissions enforced at query time, and citations on every claim. Work is contracted through a US entity with full IP assignment.

RAG Deployment specifics

  • Retrieval is evaluated separately against labeled question-document pairs, because that is where most failures originate.
  • Permissions are carried into the index and enforced at query time rather than filtered after generation.
  • Every claim cites its source passage and version, and the system abstains when retrieval confidence is low.
  • Embedding, storage, and query costs are modeled against corpus growth with retention and tiering planned.

How FISTA engineers

  • Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
  • AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
  • Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
  • One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.

What you get as a client

  • 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
  • A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
  • US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
  • Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.

Clear answers

What platform teams ask before deploying AI.

Straightforward guidance for evaluating scope, fit, and the next step.

01Why does our RAG system give wrong answers?

Usually retrieval: the right passage was never fetched, so the model answered from a poor context. Measuring retrieval separately with a labeled question set is the first diagnostic step and usually the fix.

02How do you keep the index current?

Change detection on sources with incremental reindexing, plus freshness indicators surfaced in answers so users know when content was last updated.

03Can RAG respect our access controls?

Yes, by carrying permissions into the index and enforcing them at query time against the asker's identity. Post-filtering generated answers is not sufficient, because the model has already seen the content.

04Which vector database should we use?

Often the one your stack already supports, since retrieval quality depends far more on chunking, hybrid search, and reranking than on the store. FISTA recommends against your scale and existing infrastructure.

05How long does RAG deployment take?

A focused corpus typically reaches production within weeks; enterprise-wide deployment builds domain by domain, with permission modeling as the usual long pole.

Continue exploring

Related capabilities

RAG development servicesBuilding the retrieval system before deployment.Knowledge and RAG agentsAgents grounded in your retrieval layer.Why RAG systems hallucinateField note on retrieval failure modes.

Scoped in writing before you commit

Fix retrieval, and the answers fix themselves.

Bring your corpus and real user questions. The scoping call returns a retrieval design, an evaluation plan, and a cost model.

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