Choose whether to share optional analytics.

FISTA Solutions does not load Google Analytics until you accept. Rejecting keeps optional analytics off. Read the Cookie Policy.

RAG Systems

RAG Development

FISTA Solutions builds RAG systems where retrieval is engineered rather than assumed: chunking tuned to your document structure, hybrid keyword and semantic search with reranking, permission-aware retrieval, citation-backed answers, and evaluation that measures retrieval quality separately from generation.

Start an AI project Get a scoped quote
150+
projects delivered
50+
companies served
99.9%
verified uptime
47%
efficiency gains
12+
countries reached

What we build

What does RAG development include?

RAG development covers source ingestion and change detection, chunking and embedding strategy tuned to your content, hybrid retrieval with reranking, permission filtering at query time, cited generation with abstention, and evaluation harnesses for both stages.

  1. 01

    Ingestion and chunking

    Chunking tuned to document structure with metadata preserved, because naive splitting destroys retrievability.

    Index
  2. 02

    Hybrid retrieval

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

    Retrieval
  3. 03

    Permission filtering

    Access control applied at query time against the asker, never as a post-generation filter.

    Security
  4. 04

    Cited generation

    Answers that quote and link source passages, with abstention when retrieval confidence is low.

    Answers
  5. 05

    Two-stage evaluation

    Retrieval scored on recall and precision, generation scored separately, so failures are diagnosable.

    Quality

Requirements

Which requirements shape RAG development?

RAG quality is retrieval quality. Requirements cover chunking that preserves meaning, hybrid retrieval tuned on real questions, permission enforcement in the index, citation discipline, and evaluation that separates retrieval failures from generation failures.

RAG Systems: requirements and how FISTA Solutions builds to them
RequirementWhy it mattersHow FISTA builds to it
Chunking strategyNaive splitting breaks retrievability.Structure-aware chunking with overlap and metadata, tuned against your document types.
Hybrid retrievalSemantic search alone misses exact matches.Keyword and semantic retrieval combined with reranking, tuned on labeled questions from real users.
Permission enforcementRetrieval can leak restricted content.Permissions carried into the index and applied at query time against the asker's identity.
Citation disciplineUnverifiable answers are not trusted.Every claim cited to passage and version, with links back to the source document.
Diagnosable evaluationOne score hides where the failure is.Retrieval and generation evaluated separately, so fixes target the actual problem.

Where AI fits

How should you sequence RAG development?

Build RAG from the questions backwards: collect the questions people actually ask, label which documents answer them, tune retrieval against that set, and only then worry about generation — because generation cannot fix a missing passage.

  1. 01

    1. Collect real questions

    The questions users actually ask, not the ones the team imagines they will.

  2. 02

    2. Label the answers

    Which documents or passages should answer each, creating the retrieval benchmark.

  3. 03

    3. Tune retrieval

    Chunking, hybrid search, and reranking iterated against measured recall on that set.

  4. 04

    4. Add citations and abstention

    Answers that quote sources and decline when retrieval confidence is low.

  5. 05

    5. Track unanswered questions

    The failure log is the content backlog, and it is usually the fastest quality improvement available.

Cost and timeline

How much does RAG development cost, and how long does it take?

Cost is driven by corpus size, document variety, and evaluation depth; timeline by source access and question labeling. FISTA does not quote blind: the scoping call returns a retrieval design and an evaluation plan.

Retrieval tuning is where the effort goes and where the results come from. Most disappointing RAG systems have adequate models and poor chunking, ranking, or coverage.

Question labeling takes domain time and is the highest-leverage input to the project. Without it, retrieval tuning is guesswork and quality claims cannot be defended.

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.

Get a scoped quote

Delivery

How does FISTA deliver an AI system?

FISTA delivers AI in four phases: a discovery sprint that defines the success metric, data readiness, and specification; a design that fixes the model strategy, retrieval, guardrails, and evaluation plan; iterative builds scored against a golden set; and a production release with tracing, dashboards, cost budgets, and a change process.

  1. 1

    Discover and define

    Use-case selection, data audit, success metrics, risk review, and a written specification with an evaluation plan.

    Output

    Specification, golden set, estimate

  2. 2

    Design the system

    Model strategy, retrieval and data pipelines, guardrails, human review points, and the deployment target.

    Output

    Architecture, model decision record

  3. 3

    Build and evaluate

    Two-week increments, each scored on the evaluation harness for quality, latency, and cost, demoed on real data.

    Output

    Eval reports, working system

  4. 4

    Release and monitor

    Production deployment with tracing, quality and cost dashboards, drift alerts, runbooks, and a change process that re-runs the evals.

    Output

    Production AI system with SLOs

Why FISTA

Why choose FISTA Solutions for RAG development?

FISTA engineers retrieval first, measures it independently, and builds citation and abstention behavior so answers are verifiable. Work is contracted through a US entity with full IP assignment.

RAG Systems specifics

  • Retrieval is evaluated on labeled question-document pairs before generation quality is considered at all.
  • Hybrid keyword and semantic retrieval with reranking is tuned against real user questions rather than defaults.
  • Permissions are enforced in the index at query time, so retrieval cannot surface content the asker may not see.
  • Answers cite passage and version, and the system abstains rather than answering from weak context.

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 buyers ask before an AI build.

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

01Why does our RAG system answer badly?

Almost always because the right passage was not retrieved. Measuring retrieval separately against labeled questions identifies whether the problem is chunking, ranking, or missing content — and it usually is not the model.

02Which vector database should we use?

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

03How do you handle documents that change?

Change detection with incremental reindexing, plus version awareness so answers cite the version they relied on and superseded content is demoted.

04Can RAG work across permissions?

Yes, with access controls carried into the index and applied at query time against the asker. Post-filtering generated answers is insufficient because the model has already read the content.

05How long does RAG development take?

A focused corpus typically takes weeks including retrieval tuning, with question labeling and source access as the usual gating items.

Continue exploring

Related capabilities

RAG deployment servicesTaking a retrieval system into production operation.Knowledge and RAG agentsAgents built on top of the retrieval layer.Enterprise RAG costField note on what RAG actually costs to run.

Scoped in writing before you commit

Retrieve the right passage, then the answer is easy.

Bring your corpus and the questions people ask. The scoping call returns a retrieval design and an evaluation plan.

Start an AI project Browse all ai development