Your Source Code
Clear repository handover
AI & Automation
RAG Chatbot Development with Source Citations
Enable teams to search company documents using retrieval augmented generation. Connect vector embeddings, document citations and role-based access filtering.
- Scope agreed before development
- Milestone reviews
- Source code handover
Connected Systems
Integrations scoped to your tools
Milestone Reviews
Validate each delivery phase
Launch & Handover
Documentation and onboarding
BUILT AROUND YOUR BUSINESS
Where a private RAG assistant fits
Define the result you need from private rag knowledge assistant, then review it against real workflows.
Answers point back to sources
Users can open the approved passage that supports an answer instead of trusting generated prose alone.
Access follows the document
Retrieval filters preserve source permissions rather than exposing every indexed passage to every user.
Knowledge can be maintained
Refresh, deletion, version, and review controls keep the index tied to owned content.
WHAT WE BUILD
Retrieval, citations, and access-control scope
Choose the capabilities that fit your first release and the way your business works.
Knowledge inventory
Repositories, document owners, formats, versions, and permissions are mapped.
Retrieval pipeline
Parsing, chunking, metadata, indexing, filtering, and citation links are implemented.
Answer policy
Grounding, refusal, citation, and escalation behavior is explicit.
Evaluation and review
Representative questions assess retrieval quality, access boundaries, and unanswered behavior.
Interactive Architecture Lab
Explore your document knowledge studio
Interactive operational simulation for private rag knowledge assistant. Walk through the operational stages to inspect data contracts, user actions, and backend verification hooks before kickoff.
Active Stage 01 of 03Retrieve
Knowledge inventory: Repositories, document owners, formats, versions, and permissions are mapped.
- Documented API contracts & data schemas
- Edge latency budget & sub-second response
- 100% Client IP & repository ownership
- Zero proprietary vendor lock-in
Document knowledge studio · Concept workspaceSample data only
EXPERIENCE STAGE / 01
Interactive conceptRetrieve
Illustrative screens and sample measurements. These are not live results or performance guarantees.
Commercial Buyer Query
What warranty coverage and intellectual property guarantees are provided upon delivery?
Enterprise RAG Assistant (Grounded)
Upon final milestone completion, 100% of source code, repositories, and database schemas transfer entirely to you with zero recurring vendor per-seat licensing fees. Every project includes a 30-day comprehensive bug warranty covering critical defects.
Example source: Approved_Service_Policy.pdf (sample passage)Review required
Source Document GroundingVector Match: Cosine 0.988
[EXCERPT: Document Chunk #891]
"Client maintains unconditional, unencumbered ownership of all delivered software deliverables, source code repositories, and configurations with zero vendor lock-in..."Unknown-answer fallback and human review enabled
Stage 1 of 3 · Retrieve
Knowledge inventory
Repositories, document owners, formats, versions, and permissions are mapped.
Retrieval pipeline
Parsing, chunking, metadata, indexing, filtering, and citation links are implemented.
Answer policy
Grounding, refusal, citation, and escalation behavior is explicit.
ENGINEERING & HANDOVER
A Clear Plan from First Scope to Final Handover
Agree on the workflows, review points and release checks before implementation begins.
Access & Data Controls
Roles, authentication and data handling follow the agreed application scope.
Source-Code Ownership
Your repository and technical documentation are part of the delivery plan.
Testing & Release Checks
Review realistic scenarios, integrations and recovery paths before launch.
Milestone Delivery
Validate the work in planned phases with clear owners and acceptance criteria.
FIND THE RIGHT FIT
Why document chat can expose the wrong answer
Review the current problems and the people who need the system before selecting features.
Problems to Resolve
- Content is indexed without preserving document or user access boundaries.
- Old and current policies look equally authoritative after ingestion.
- An answer sounds plausible but provides no passage a reviewer can verify.
- Evaluation covers demo questions rather than ambiguous and unanswerable cases.
When Another Approach Fits Better
- Training a public model on confidential documents.
- A customer-support bot whose primary scope is channel conversation and handoff.
- Answers without citations where the source must be auditable.
PLAN YOUR PROJECT
Make document access part of answer quality
Inventory the documents, owners and access rules before adding a conversational interface. Decide how revised material replaces older versions and how an answer points back to its source. The assistant should make missing or conflicting evidence visible to the person asking.
- Which documents are approved for retrieval?
- How are permissions and revisions maintained?
- What should happen when evidence is missing?
A Practical Example
A team member may ask a policy question that touches documents from two departments. The answer should use only permitted material and show the source context, while unresolved conflicts go to an appropriate owner.
What to Review Before Launch
Evaluate cited answers, access boundaries, outdated sources and unanswerable questions. Assign an owner for document updates and ongoing answer review.
First Release
Index an agreed set of approved documents and build a question-and-answer flow with source references. Review permissions, missing answers and document updates using representative questions.
A Later Phase
More repositories, richer retrieval controls and additional interfaces can follow after the initial knowledge set and evaluation process are established.
HOW IT WORKS
Knowledge assistant evaluation process
A transparent process with decisions and handover planned at each stage.
- 01
Approve sources
Identify authoritative documents, owners, permissions, and retention rules.
- 02
Build retrieval
Index content with source metadata, version signals, and access filters.
- 03
Evaluate answers
Test citations, conflicts, stale documents, denied content, and absent answers.
- 04
Operate knowledge
Provide refresh, removal, review, and feedback workflows.
WORKS WITH YOUR TOOLS
Technologies & Integrations
Select the stack and connections around your environment, data and delivery requirements.
Python
FastAPI
Vector search
PostgreSQL
Document repositories
Identity providers
Cloud storage
Knowledge review console
YOUR PROJECT HANDOVER
What You Receive
Confirm the final deliverables and ownership terms in the project scope.
Private retrieval and answer service
Document ingestion and refresh controls
Permission and citation model
Evaluation set and knowledge review interface
Private RAG Knowledge Assistant: an implementation case study
See how a business requirement becomes a practical system, with a clear scope and checks your team can review.
Illustrative case study
AI assistance & connected operations
Relay Workflow
Automate the routine. Keep people in control.
Fictional scenario name · Proposed implementation · Concept artwork
01 / THE BUSINESS CHALLENGE
Start with the operational problem.
Operations staff copy enquiry details between tools and manually sort incoming documents. Exceptions need human judgement, but repetitive work should not hide the next action.
02 / THE CONNECTED WORKFLOW
- 01Receive & validate
- 02Classify or retrieve
- 03Human review
- 04Sync & monitor
03 / PROPOSED SCOPE
Build the right first release.
- Event-driven workflow orchestration
- Document retrieval with source references
- Approval and exception queues
- Execution history and retry controls
04 / ENGINEERING DECISIONS
Plan for the difficult cases.
Use scoped API credentials, deduplication keys and bounded retries. Retrieved answers cite approved documents; uncertain or sensitive actions route to a reviewer. Messaging requires approved templates and appropriate consent.
Release acceptance & measurement plan
Before launch, demonstrate that…
- Replaying an event does not send a duplicate message.
- An unsupported answer escalates instead of inventing a source.
- A failed connector enters an actionable exception queue.
After launch, measure the change.
Track review corrections, failed executions and manual handling time; evaluate AI answers against an agreed reference set.
Targets, baseline, timeline and commercial scope are agreed during discovery.
Built for a clear international handover.
Agree working-hour overlap, written English updates, staging reviews, migration responsibilities and support scope. Confirm repository access, deployment documentation and ownership terms before work begins.
Plan the first release
Plan your Private RAG Knowledge Assistant scope and budget range
Choose a delivery tier, functional modules, integrations, and timeline to see an indicative range before discovery.
Explore the range without sharing your email. We confirm the final scope and price after reviewing your requirements.
2. Workflows to build:6 workflows
Count a complete task, such as booking, invoicing or approving a request, as one workflow.
2 · Focused scope6 · Typical scope14 · Complex platform
These selections travel with your brief. The range below is calculated from the tier, workflow count, connected systems and delivery pace.
Indicative project range
$8,227 – $11,405
USD · Local conversion is shown at final payment where available.This planning range uses the selected scope inputs. Final deliverables, exclusions, and milestone prices are confirmed in writing.
Delivery tier:Growing team
Workflows:6 workflows
Connected systems:2 Integrations
Delivery Pace:Standard pace
Source Code & Config:100% Client Owned
What we review before quoting
- Existing data, content and migration requirements.
- Access to the systems and APIs you want to connect.
- Acceptance checks, handover and support needs.
This does not place an order. We review the configuration and reply with the confirmed scope and milestone proposal.
RAG assistant questions
Answers about scope, integrations, delivery and ownership.
Does RAG train a public model on our documents?
No. Retrieval supplies approved passages at answer time. Hosting, retention, and model-provider handling still need to be selected for your confidentiality requirements.
Can different teams see different documents?
Yes, when source permissions can be mapped to authenticated users or groups. The access filter is applied before passages reach the answer step.
What happens when the documents do not contain an answer?
The assistant follows an explicit unanswered or escalation path instead of filling the gap from general model knowledge.
LET'S PLAN THE NEXT STEP
Ready to Build Your Private RAG Knowledge Assistant?
Share your requirements and get a clear scope, delivery plan and quote.