Traceable RAG Development Services
Retrieval sounds like a solved problem until you point it at a real business. Then it meets a decade of documents nobody curated, three versions of the same policy with no way to tell which is current, and a permissions model that was never designed for a system that reads everything at once. The model is rarely what fails. The content is.
Cabot provides RAG development services that start with the state of your material and end with answers a reviewer can follow back to the page they came from. We assess what you have, choose the retrieval approach that actually fits it, respect your permissions at query time, and measure whether the citations hold up. If your content is not ready, we tell you that first, because no amount of model quality repairs a corpus nobody can trust.
Content assessment · Hybrid retrieval · Permission-aware · Citations · Evaluation | Model-neutral and security led
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50%
Of organizations will adopt a zero-trust posture for data governance by 2028 as unverified AI-generated content spreads through corpora
3x
More use of small, task-specific models than general-purpose large language models expected by 2027, which favors grounding over scale
700+ projects
Delivered for 140+ clients since 2010, across the document estates this work has to read
What is RAG, and when is it the right answer?
Retrieval-augmented generation is a way of answering a question by first finding the relevant passages in your own content, then having a model answer using only what it found, with the source attached. It is worth separating from the two things it gets confused with. Fine-tuning changes how a model writes and what patterns it has internalized, but it does not give the model access to a document published this morning. Plain search returns a list of results and leaves the reading to you. Retrieval sits between them: current, because it reads live content rather than training data; specific, because the answer is drawn from your material; and checkable, because every claim can carry the passage it came from. That last property is the one that matters most in a regulated conversation, and it is why we treat citation quality as a feature rather than a nicety.
Why retrieval systems give confident, wrong answers
Three versions of the truth. The old policy, the revised policy, and someone's summary all sit in the index, and nothing marks which one is current.
Chunking cut the meaning in half. A passage split mid-table or mid-clause retrieves as nonsense, and the model fills the gap with something plausible.
Permissions were not carried into retrieval. The system indexes everything, so it can surface a document the person asking was never entitled to read.
The index went stale. Content changed, the pipeline did not run, and answers now describe a process the business abandoned months ago.
Citations point at the container, not the claim. Linking to a 200-page PDF is not a source, because nobody is going to find the sentence.
Nobody measured retrieval separately. When an answer is wrong, no one can say whether the search missed the passage or the model misread it.
What we build with retrieval
Content readiness assessment
We inventory the material, test how well it retrieves against real questions, and tell you plainly where it needs work before a build is worth funding.
Retrieval architecture
We choose between keyword, vector, hybrid, and graph retrieval on evidence from your own content rather than on which approach is currently fashionable.
Pipeline and indexing
We build the ingestion, parsing, chunking, and refresh that keep the index current, including the unglamorous work of getting clean text out of PDFs and scans.
Permission-aware retrieval
We carry your access model into the query itself, so a person only ever retrieves what they were already entitled to read.
Citation and answer quality
We make sources precise enough to check, pointing at the passage rather than the document, and measure whether answers are actually supported by what was retrieved.
Evaluation and operations
We score retrieval and generation separately so failures are diagnosable, then monitor drift, freshness, and cost once it is running, alongside our LLMOps practice.
What do RAG development services cost to build and to run?
Build cost is driven less by the retrieval engine than by the condition of your content, because parsing, cleaning, and structuring a difficult corpus is the bulk of the work. Running cost is per query and scales with volume and how much context each answer needs. We scope both up front, and we tell you if content preparation will dominate the estimate rather than discovering it later.
How we get retrieval right before we build on it
Start from the questions, not the documents
We collect the real questions people ask, including the awkward ones, and use them as the test set. A corpus that answers those is ready. A corpus that does not is a project of its own, and better named early.
Test retrieval on its own
Before any model writes an answer, we measure whether the right passage comes back at all. Separating retrieval from generation is what turns a vague complaint into a specific fix.
Treat currency as a first-class problem
We establish which source wins when versions conflict, and how often the index refreshes, because a confident answer from a superseded document does more damage than no answer.
Make every claim checkable
We cite at passage level and design the interface so following a source takes one click. If a reviewer cannot verify an answer quickly, they will stop trusting all of them.
The stack behind the systems we ship
Target stack
Retrieval and indexing
Ingestion and models
Deployment and operations
What happens at each stage
How we choose and govern the tooling
Which retrieval approach fits your content?
Built for the way your industry is judged
Healthcare
We know a clinical answer has to come from an approved source and nothing else, so we constrain the index to material you have sanctioned and keep protected health information inside your environment.
Financial services
We understand an auditor will ask which document an answer came from and whether it was current, so we keep citations precise and version history intact.
Legal and professional services
We know a fabricated citation is a professional liability, so we surface the passage behind every claim and flag answers the retrieved material does not support.
Industries we already understand
Healthcare
Ecommerce
Fintech
Travel and Tourism
Security
Automobile
Stocks and Insurance
Restaurant
Built to pass review from users, auditors, and security teams
Retrieval creates a specific risk that other systems do not: it reads everything you point it at, and then answers questions about it. We build the controls into the work rather than bolting them on afterward, so what we deliver can face users, auditors, and security teams without another round of rework.
Which standards apply depends on the market you operate in. The security practices below apply to every engagement. The regulatory items apply where your system handles the data they govern, and we scope that with you during assessment.
Permission-aware retrieval deserves a specific mention. The common failure is indexing everything with a service account that can read it all, which quietly turns a search box into a way around your access model. We carry entitlements into the query so results are filtered for the person asking, not filtered afterward.Where governance extends beyond retrieval to the whole estate, see our data governance for AI practice.
How we get you from scattered content to answers you can cite
Our RAG development services follow a structured path from a pile of documents to a retrieval system in production, with a decision point at every step and something you can judge at the end of each.
1. Collect the real questions
We gather what people actually ask, including the edge cases, and turn them into the test set everything else is measured against.
2. Assess the content
We profile your material for structure, duplication, version conflicts, and extractability, and tell you plainly what needs fixing before a build is worth funding.
3. Prove retrieval on your material
We test whether the right passages come back for your real questions, so you fund the full build on measured results rather than a demo.
4. Set the quality bar
Together we define the retrieval and answer scores the system has to clear, and how citations must be presented, and you approve both.
5. Build, secure, and integrate
We build the pipeline and interface into your workflow with permissions carried into every query, reviewing with you in short cycles.
6. Release, refresh, and improve
We launch to a controlled group, keep the index current, watch quality and cost, then widen it on what real usage reveals.
The team that owns whether the answers hold up
Retrieval projects go wrong when the people who understand the content and the people who understand the machinery never sit together. On our engagements that gap does not exist. An AI engineer owns retrieval strategy, chunking, and how answers are assembled. A data engineer owns ingestion and the refresh that keeps the index honest. A security engineer owns how entitlements are carried into the query. A QA lead owns the question set and the pass mark, and has the authority to hold a release.
Our depth shows in four specific places. We are strong at getting clean text out of documents that were never meant to be machine-read. We are strong at chunking decisions that preserve meaning, which is where most retrieval quality is won or lost. We are strong at permission-aware retrieval across systems with inconsistent access models. And we are strong at building evaluation that separates a retrieval failure from a generation failure. We do not claim to be equally deep in everything, and we will tell you when your content needs work before a system does.
We work as an extension of your team, not a black box down the hall. You see the evaluation scores, the content findings, and the cost per query, and you own the code and the IP from the first commit. When you need more hands as the work scales, you can add forward-deployed engineers to the same team rather than starting over with a new one.
Why enterprise leaders choose Cabot for traceable RAG development
We assess the content before we sell the build
If your material will not support reliable retrieval, we say so at the start, even though the honest answer is a smaller first engagement than either of us expected.
Every answer carries a checkable source
We cite at passage level, not document level, so verifying a claim takes one click. That is the difference between a system people trust and one they quietly stop using.
Permissions are carried into the query
Results are filtered for the person asking rather than after the fact, so retrieval never becomes a way around the access model you already have.
Retrieval and generation are measured separately
When something goes wrong you get a diagnosis rather than a shrug, because we know whether the passage was missed or misread.
Built to stay current
Refresh, version precedence, and drift monitoring are part of the first release, because a retrieval system that quietly goes stale is worse than none.
A full engineering practice behind it
Model selection, agents, and operations each have a deeper practice within our AI engineering services, so the work does not stop at the edge of one specialty.
Where to go next, depending on what you need
You need it to act, not just answer
If the system should complete tasks across your tools rather than return answers, look at AI agent development.
You are still choosing the model approach
If the open question is whether to ground, tune, or integrate, start with our LLM development services.
You are earlier than all of this
If you are still shaping what to build with AI at all, start with our generative AI development services.
Our Clients
Retrieval-augmented generation is a way of answering a question by first finding the relevant passages in your own content, then having a model answer using only what it found, with the source attached. It keeps answers current, because it reads live content rather than training data, and checkable, because every claim can carry the passage it came from.
Build cost is driven less by the retrieval engine than by the condition of your content, because parsing, cleaning, and structuring a difficult corpus is usually the bulk of the work. Running cost is per query and scales with volume and how much context each answer needs. We scope both up front, and we tell you when content preparation will dominate the estimate. For a quick figure, try our Cost Calculator.
For most business use cases, retrieval first. Fine-tuning changes how a model writes and what patterns it has internalized, but it does not give the model access to a document published this morning, and it cannot cite a source. Retrieval gives you both. Fine-tuning earns its place when you need a specific format, tone, or specialist vocabulary that context alone cannot supply, and the two are often combined.
In our experience it is almost always the content rather than the model. The usual causes are several versions of the same document with nothing marking which is current, chunking that split a passage mid-table or mid-clause, an index that has gone stale, or retrieval that simply never returned the right passage. The fix starts with measuring retrieval separately from generation, because until you know which one failed you are guessing.
We carry your access model into the query itself, so the system only ever retrieves what the person asking is already entitled to read. The common failure is indexing everything under one service account and filtering afterward, which quietly turns a search box into a way around your permissions. We also log every query and answer so access can be reviewed later.
A focused use case on reasonably clean content usually reaches a tested working version in a few weeks and a production release inside a quarter. Content in poor condition is what extends timelines, which is why we assess it before quoting the build rather than after. We agree the scope and the quality bar during the readiness review.
Documents, wikis, tickets, transcripts, structured records, and the awkward middle ground of scanned PDFs, spreadsheets, and diagrams. Extractability varies enormously, and that variation is what the content assessment measures. We would rather tell you a source is not usable in its current form than quietly index it badly.
We build refresh into the pipeline rather than treating indexing as a one-off, establish which source wins when versions conflict, and monitor for drift in answer quality. A retrieval system that silently goes stale is more dangerous than one that is obviously missing, because people keep trusting it.