Permission-aware internal search
Retrieve policies, procedures, and product documentation without exposing records a user cannot access. Show the source and provide a clear fallback when the available material cannot support an answer.
RAG Development & LLM Integration Services
Make approved company knowledge useful inside your product or internal workflow. We build retrieval-augmented generation (RAG), document search, and LLM integrations with source citations, access controls, and measurable answer quality.
Direct access to the builder. Written scope before implementation. Code and deployment handed over.
Practical applications
What we can help you build or improve
Document extraction and review
Convert incoming documents into structured fields and a review queue. Validate the output against the required schema and route missing or contradictory information to a person.
LLM features in an existing application
Integrate model providers through an application layer with usage limits, error handling, and evaluations. Keep customer access and billing independent from provider-specific prompts and APIs.
Buying triggers
When this engagement becomes useful
A scope usually starts with one or more of these situations, not a request for a particular framework.
An existing SaaS or internal product needs AI features without replacing the rest of the application.
A retrieval prototype returns plausible answers, but source quality, permissions, and evaluation are unclear.
Unstructured documents or messages need to become validated data inside a business workflow.
Model cost, latency, provider failure, and output quality are not visible enough to operate the feature.
What the work covers
We build knowledge systems around the job a person needs to complete, not around the existence of a vector database. The design starts with source ownership, access permissions, update frequency, answer expectations, citation requirements, unsupported-answer behavior, and the decisions that may follow an answer.
The implementation can combine document ingestion, metadata and access-control propagation, hybrid retrieval, reranking, structured extraction, cited generation, and product interfaces. Model and retrieval choices are compared on representative questions and documents using quality, coverage, latency, privacy, and operating cost.
Production operation includes source refresh and deletion behavior, evaluation cases, feedback capture, observability, fallback paths, and ownership when an answer is incomplete or a source is unavailable. Retrieval can improve grounding, but the system still needs a visible boundary for what it does not know.
Best For
Organizations and product teams that need AI to work with internal documents, records, policies, product data, or domain knowledge without losing source context.
Typical tools & methods
Hybrid retrievalRerankingVector searchOpenAI / AnthropicEvaluation sets
Related integration work
WriteAI.me: model integration in a SaaS product
Review the multi-provider integration, workspace access, and subscription architecture. This is evidence of product and LLM integration work, not a claim that this project includes enterprise RAG.
Read the scope and approachIntended progress
What should work better afterward
A product-native AI workflow
AI behavior fits the existing user journey, data model, permissions, and interface instead of becoming a disconnected demo.
Quality that can be checked
Representative inputs, expected behavior, source evidence, and failure cases form a repeatable evaluation process.
Controlled operating cost
Routing, caching, limits, latency, fallback behavior, and provider usage are measured against the real workload.
What you receive
- A model and architecture decision record based on accuracy, latency, privacy, and cost
- The agreed retrieval, extraction, generation, or tool-calling product workflow
- A versioned evaluation set and repeatable quality/cost measurements
- Fallback behavior, monitoring, documentation, and team handoff
How acceptance is decided
- 01Outputs satisfy the agreed evaluation rubric on representative data.
- 02Invalid structured output, model timeout, and provider failure paths are handled safely.
- 03Token usage, latency, errors, and the product-specific quality signal are observable.
Scope boundaries
Where a different engagement may be needed
- Retrieval can improve grounding, but it does not guarantee a correct answer or repair weak source data.
- Model and vector database charges are external operating costs and remain visible separately from implementation.
- Private data is connected only after access boundaries, retention, provider terms, and deployment requirements are reviewed.
Before you commit
Scope, cost, and the first useful step
Start with a representative document set, real user questions, and source permissions. Compare retrieval and answer quality on those examples before committing to a wider knowledge rollout.
What determines the estimate?
Document formats, source connectors, update frequency, permission rules, OCR needs, and evaluation depth affect the build. Indexing, storage, model calls, and hosting are recurring costs to estimate separately.
What should you bring to the first conversation?
Bring a source inventory, anonymized sample documents, expected questions, and who may access each source. Include examples where the system should refuse to answer; these are as useful as successful responses.
Timing is agreed after the scope and dependencies are understood. Access to source systems, review availability, procurement, and migration requirements can change the schedule.
Questions before scope
Common questions before you choose this service
What does an LLM integration service include?
Depending on the use case, the scope can include model selection, prompts, structured outputs, RAG, tool calling, permission-aware data access, evaluation cases, caching, cost limits, fallback behavior, monitoring, and product interface work.
Can you add RAG to an existing SaaS product?
Yes. We review source ingestion, chunking, metadata, permissions, retrieval, reranking, citations, evaluation, and update behavior so the feature fits the existing product and access model.
Do you only work with OpenAI?
No. Provider choice follows task quality, latency, privacy, deployment constraints, and total operating cost. The architecture can isolate provider-specific behavior when switching or routing is a realistic requirement.
How do you reduce hallucinations?
There is no universal elimination method. We constrain the task, improve source retrieval, require structured outputs where appropriate, expose citations, add validation and refusal behavior, and measure the remaining error rate on representative cases.
Can you work with a US or UK team remotely?
Yes. ZamDev AI is based in Lahore, Pakistan and works remotely with international teams. We agree meeting overlap, the decision-maker, a written review cadence, repository access, and deployment ownership during scoping. Tell us your time zone and any procurement, hosting, or data-location requirements so we can confirm the fit before a commitment. We do not represent a US or UK office.
Related engineering guides
Review the implementation trade-offs before deciding whether this engagement fits your product.
Start with a written scope for the work.
Share the current product, workflow, or repository and the decision you need to make. We will identify the useful next step, required access, deliverables, exclusions, and acceptance criteria.