POC Timeline From data access to a queryable, evaluated proof-of-concept
Full Production Build End-to-end from discovery to live deployment
Cited Responses Every answer tied to a source document or database record
Security Standard SOC 2, GDPR, HIPAA & permission-aware retrieval built in
A unified assistant that sits across your organisation’s knowledge base — internal wikis, policy documents, project files, and product documentation — answering questions in natural language with cited sources. Connects to SharePoint, Confluence, Google Drive, S3-compatible stores, and custom API sources.
Upload contracts, reports, research papers, or any unstructured document set and interrogate them directly. Powered by a RAG pipeline tuned for high-precision retrieval over dense, domain-specific text. PDFs with mixed layouts, scanned documents, and complex HTML with embedded tables all handled in the ingestion layer.
An agent-augmented support assistant that retrieves relevant product documentation, past ticket resolutions, and knowledge base articles to generate accurate, consistent first responses — reducing escalations and average handle time. Delivered as an agent overlay or self-service bot.
A semantic search layer over your existing content repositories. Users describe what they need in natural language; the system retrieves by meaning, not just keyword match, with relevance-ranked results and document previews. Replaces fragmented intranet search with a single intelligent access point.
Business users ask questions in plain language — “Which regions missed target last quarter and why?” — and the system translates them into live SQL queries against your data warehouse, returns the numbers with a clear explanation, and links back to the underlying records. No SQL skills required.
Vertical-specific assistants built on RAG — each tuned to domain vocabulary, retrieval precision requirements, and compliance constraints. Legal copilots query case law and contract archives. Clinical copilots retrieve treatment protocols. Financial copilots surface regulatory filings and portfolio data
A unified assistant that sits across your organisation’s knowledge base — internal wikis, policy documents, project files, and product documentation — answering questions in natural language with cited sources. Connects to SharePoint, Confluence, Google Drive, S3-compatible stores, and custom API sources.
Upload contracts, reports, research papers, or any unstructured document set and interrogate them directly. Powered by a RAG pipeline tuned for high-precision retrieval over dense, domain-specific text. PDFs with mixed layouts, scanned documents, and complex HTML with embedded tables all handled in the ingestion layer.
An agent-augmented support assistant that retrieves relevant product documentation, past ticket resolutions, and knowledge base articles to generate accurate, consistent first responses — reducing escalations and average handle time. Delivered as an agent overlay or self-service bot.
A semantic search layer over your existing content repositories. Users describe what they need in natural language; the system retrieves by meaning, not just keyword match, with relevance-ranked results and document previews. Replaces fragmented intranet search with a single intelligent access point.
Business users ask questions in plain language — “Which regions missed target last quarter and why?” — and the system translates them into live SQL queries against your data warehouse, returns the numbers with a clear explanation, and links back to the underlying records. No SQL skills required.
Vertical-specific assistants built on RAG — each tuned to domain vocabulary, retrieval precision requirements, and compliance constraints. Legal copilots query case law and contract archives. Clinical copilots retrieve treatment protocols. Financial copilots surface regulatory filings and portfolio data
Every factual claim is tagged to the specific document, section, or database record it was drawn from. Users click through to the source. Compliance teams get a full retrieval trace — not a black box answer.
Integrated with Entra ID, Okta, and Google Workspace. Retrieval is scoped to the querying user's authorised document set — enforced at the retrieval layer, not as a post-generation filter. A finance analyst and a CFO get different answers from the same system.
Cloud-native, VPC/private cloud, or fully on-premises and air-gapped. Open-weight models (Llama 3, Mistral, Qwen) served locally via vLLM for environments where external API calls are prohibited. Your data never leaves your perimeter.
Cloud-native, VPC/private cloud, or fully on-premises and air-gapped. Open-weight models (Llama 3, Mistral, Qwen) served locally via vLLM for environments where external API calls are prohibited. Your data never leaves your perimeter.
Ragas evaluations on a scheduled cadence. LangSmith/Langfuse full trace observability. Low-scoring queries root-caused and resolved. Model provider updates regression-tested before rollout. A system that improves over time, not one that degrades silently.
A live, queryable proof-of-concept in three to four weeks — evaluated against a golden dataset of your representative queries, with a signed go/no-go recommendation before any full-build commitment. No slide decks without working software first.
LangChain
LlamaIndex
Azure OpenAI
vLLM / Ollama
pgvector
Pinecone
Qdrant
Weaviate
Milvus
Ragas
LangSmith
Langfuse
Azure OpenAI
AWS Bedrock
Google Vertex AI
Llama 3 / Mistral / Qwen
SharePoint
Confluence
Google Drive
S3-Compatible Stores
SQL Databases
Custom API Sources
Agents and self-service bots retrieve the exact product documentation and past resolution notes relevant to each issue — generating a cited, structured response in under two seconds. Escalation rates fall. Handle time falls.
HR teams deploy a RAG assistant over policy documents and HR handbooks. IT helpdesks give staff instant access to setup guides, VPN instructions, and access-request procedures — always drawn from the current document version.
Sales reps ask in natural language for competitive comparisons, product capabilities, or pricing policy details during a live call. The RAG assistant retrieves from approved documentation — not from model memory — ensuring responses are consistent with current positioning.
Legal teams query large contract archives to surface obligation clauses, renewal dates, liability caps, and governing law provisions across hundreds of agreements simultaneously — with a direct link to the source document and page number.
Which accounts in the North region have had no contact in the last 60 days and have a renewal due this quarter?" The RAG system translates this into a live CRM query, returns a ranked list, and explains the query logic. No SQL skills required.
Compliance officers query regulatory document libraries — FCA, SEC, PRA policy updates, internal policies — with permission-aware retrieval that ensures each user accesses only their authorised document set and every query is logged for audit.
Replaces a static retrieval step with an agent that plans its retrieval strategy. For multi-part queries — "Compare our EMEA pricing policy from 2023 with the current version and flag any changes that affect enterprise tiers" — the agent issues multiple targeted retrievals, synthesises across result sets, and constructs a structured answer. Built on LangChain and LlamaIndex agent interfaces with explicit state management for multi-step retrieval chains.
Represents the knowledge base as a graph of entities and relationships rather than a flat chunk store. When questions require understanding how entities relate to each other — organisational structures, supply chain dependencies, regulatory cross-references — graph traversal retrieves more contextually complete information than vector similarity can. Particularly effective for legal, compliance, and enterprise knowledge management use cases.
Extends retrieval beyond text to images, diagrams, and tables embedded in documents. Technical manuals, financial reports, and product catalogues contain critical information in non-text formats. We build ingestion pipelines that extract and index these elements, and retrieval pipelines that return them as grounding context alongside text chunks — enabling answers that correctly reference figures, charts, and structured data.
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We submit a comprehensive project proposal with estimates, timelines, CVs, etc.
Consumer AI tools do not enforce your existing access control model, do not integrate with your identity provider, and do not provide the retrieval observability or evaluation infrastructure required for production enterprise use. An enterprise RAG system is purpose-built: permission-aware retrieval, cited responses tied to specific document versions, PII redaction, full audit trails, and continuous evaluation. It is deployed within your infrastructure, governed by your security policies, and operated with contractual SLAs not a shared consumer service.
Yes. We select embedding models with multilingual support (such as multilingual-e5-large or text-embedding-3-large with language detection) and configure the retrieval and generation pipeline to handle mixed-language document sets. The LLM generation layer is prompted to respond in the user’s query language regardless of the language of the source document. For organisations with large non-English document corpora, we recommend a retrieval quality benchmark across language pairs before production deployment.
The system is explicitly instructed to return a clear, honest response when retrieved context is insufficient to answer the query rather than generating a plausible but ungrounded response. This is enforced through both prompt instruction and groundedness scoring at inference time. Unanswerable queries are logged and reviewed in the RAGOps cycle as signals for knowledge base gap analysis.
For a scoped proof-of-concept against an existing data source with defined permissions, we typically reach a queryable, evaluated POC within three to four weeks of completing the data and access assessment stage. Full production deployment timelines depend on data source complexity, identity provider integration, and compliance sign-off requirements typical full builds complete in eight to twelve weeks.
We architect the ingestion pipeline for the update frequency your data requires. For near-real-time data (live database records, pricing APIs), we build streaming or near-real-time ingestion using change data capture (CDC) patterns. For daily-updated document sets (policy documents, product catalogues), scheduled incremental pipelines detect changes and update only the affected chunks. For static archives, one-time bulkingestion with periodic refresh is appropriate. The right pattern is determined during the data and access assessment stage.
Accuracy depends on the schema complexity, the quality of schema annotations provided during setup, and the specificity of the query. In production deployments on well-annotated schemas, we consistently achieve [STAT NEEDED: text-to-SQL accuracy benchmark — cite Spider or BIRD benchmark, or internal production metric]. All generated SQL is validated against schema constraints before execution. Queries that fail validation are returned with an explanation rather than executed. We instrument query success rates as a first-class RAGOps metric and maintain a library of validated query patterns that improves accuracy over time.
RAG and fine-tuning address different problems. RAG is the right approach when you need the model to answer questions grounded in specific, frequently updated, access-controlled documents or databases it gives the model the right information at query time. Fine-tuning is appropriate when you need the model to adopt domain-specific reasoning patterns, style, or vocabulary at a level that cannot be achieved through retrieval alone. For the vast majority of enterprise knowledge access use cases, RAG delivers better accuracy with lower cost and maintenance overhead than fine-tuning.
