AI that lives inside your tools
Your team already lives in your CRM, helpdesk, team chat, and internal wiki. We embed AI directly into those tools — so your CRM scores leads automatically, your helpdesk drafts replies in your tone, your chat knows your documentation, and your internal tools answer questions grounded in your private data.
The real value of AI in 2026 comes not from standalone chat interfaces but from embedded intelligence inside the tools your team already uses. Microsoft's Work Trend Index reports that 75% of knowledge workers already use AI at work, but most are toggling between ChatGPT and their actual workflow tools — a productivity tax that defeats the point. Retrieval-augmented generation (RAG) is the architecture that connects large language models to your private data — documentation, Notion, Slack history, tickets, product databases — so the AI answers in your context with verifiable citations. According to IBM, RAG improves LLM accuracy, reduces hallucinations, and removes the need for expensive fine-tuning in most enterprise scenarios. We design, build, and ship production integrations that turn your existing stack into an AI-native workplace.
75%
of knowledge workers already use AI at work
70%
reduction in hallucinations achievable with well-designed RAG systems
3.7x
average ROI reported by enterprises on AI investments
Core capabilities
CRM AI enhancement
Leading CRMs augmented with AI lead scoring, meeting-summary auto-fill, deal insights, and next-best-action recommendations.
Helpdesk AI
Auto-drafted replies in your brand voice, ticket classification and routing, macro suggestions, and summary generation — all grounded in your past tickets and knowledge base.
Team chat bots
Bots inside your team messaging platform that search your private knowledge, summarise threads, draft announcements, pull data from your tools, and route requests to the right humans.
RAG knowledge systems
Private AI over your internal wikis, document stores, chat archives, and databases — answers every question with cited sources.
Internal AI tools
Custom internal dashboards and copilots for sales, support, HR, and operations — built for your specific workflows, not generic templates.
E-commerce AI
Smart search, product Q&A agents, personalised recommendations, AI-written product descriptions, and conversational merchandising for leading e-commerce platforms.
Why RAG is the foundation of enterprise AI
Large language models are trained on the public internet, which makes them great at general knowledge but useless for your company's private context. Fine-tuning a model on your data is slow, expensive, and brittle — every content update requires retraining. Retrieval-augmented generation solves this by indexing your documents in a vector database and retrieving the most relevant chunks at query time, then passing them to the model as context. The result: the model answers with your company's actual information, cites its sources, and updates instantly when your documents change. IBM describes RAG as "an AI framework for retrieving facts from an external knowledge base to ground large language models on accurate, up-to-date information". Every serious enterprise AI deployment we build is RAG-first.
Our integration stack
- Vector databases: leading managed and self-hosted options, chosen on scale, budget, and hosting requirements.
- Models: leading frontier and open-source large language models, chosen on privacy and cost constraints.
- Orchestration: industry-standard orchestration frameworks or custom services — whichever gives you the best balance of flexibility and maintainability.
- Integrations: leading CRMs, helpdesks, team messaging, productivity suites, e-commerce platforms, payment processors, and custom REST/GraphQL APIs.
- Authentication and security: OAuth 2.0, SAML SSO, role-based access control, data-residency controls, and audit logging for regulated environments.
Security, privacy, and compliance
Enterprise AI lives or dies on trust. We build with security-first principles: prompts and outputs are never stored by default, enterprise AI APIs are used with zero-retention agreements, sensitive fields (PII, PHI, payment data) are masked before hitting the model, and every integration includes granular role-based permissions. For regulated industries (healthcare, finance, legal) we can architect fully self-hosted RAG with open-source models inside your VPC — no data ever leaves your infrastructure. We document our security architecture formally for vendor reviews and SOC 2 audits.
Common integration patterns that deliver quick wins
The fastest wins come from embedded summary and draft features inside tools your team already uses daily. Example: a "Summarise this ticket" button in your helpdesk that reads the entire thread and pulls out the customer's issue, what's been tried, and the suggested next step. Or a "Draft reply" action in your CRM that writes a contextual email based on the contact's history, the current deal stage, and your tone-of-voice guide. These five-minute moments add up — if each person on your team saves 20 minutes a day, a 50-person team saves roughly 850 hours per month. That is typically more than the entire annual cost of the integration.
Real-world use cases
Insurance brokerage
Built a RAG system over policy documents, underwriting guidelines, and past quotes — integrated into their CRM.
Outcome: Brokers found policy information 5x faster, quote turnaround dropped from 48 hours to 4 hours, junior staff ramped in half the time.
Marketing agency
Team-chat bot grounded in client brand guidelines, past campaign data, and performance reports.
Outcome: Team pulled campaign insights in seconds instead of hunting through file storage, onboarding new account managers shortened from 6 weeks to 3.
E-commerce
E-commerce storefront with AI-powered product Q&A trained on product catalogue, reviews, and support tickets.
Outcome: Pre-purchase question response rate hit 89%, conversion rate up 14%, support tickets related to product questions dropped 31%.
Law firm
Self-hosted RAG system over 10 years of precedent documents, contracts, and internal memos — deployed inside firm VPC.
Outcome: Associates drafted first-pass memos in 90 minutes instead of 8 hours, zero data left firm infrastructure, partner review quality improved.
Our delivery process
- 1
Integration audit (1 week)
We map your current stack, identify the highest-value AI integration points, benchmark data volume and sensitivity, and confirm technical feasibility and compliance requirements.
- 2
Architecture & security design (1–2 weeks)
We design the integration architecture — RAG pipeline, vector store, model choice, authentication, access control, data masking, and audit logging. Reviewed with your IT and security teams.
- 3
Data ingestion & indexing (1–3 weeks)
We build connectors to your data sources, chunk and embed documents, handle incremental updates, and set up continuous sync. We benchmark retrieval quality against a gold-standard evaluation set.
- 4
Integration build (2–5 weeks)
We build the actual integrations — chat bot, CRM plugin, helpdesk integration, custom UI — with error handling, rate limiting, usage tracking, and user permissions.
- 5
Launch, train, iterate (ongoing)
We deploy to a pilot team, gather feedback, tune retrieval and prompts, expand to the full organisation, and provide monthly optimisation reviews.
What you get
Frequently asked questions
What is the difference between RAG and fine-tuning?
+
RAG retrieves relevant documents at query time and passes them to the model as context. Fine-tuning bakes knowledge into the model weights. RAG is faster to build, cheaper to run, easier to update (just add documents), and provides source citations. Fine-tuning is better for teaching the model a specific style or format, but not for factual knowledge that changes. For 90% of enterprise use cases, RAG is the right choice.
How does AI integration affect my existing tools?
+
We build additive integrations — your existing workflows continue to function exactly as before. AI features appear as new buttons, side panels, or slash commands inside your tools. If the AI is unavailable for any reason, the underlying tool still works. Users can always override or ignore AI suggestions.
Can you integrate with our homegrown internal tools?
+
Yes, as long as your internal tools have an API or database we can connect to. We have built integrations with custom ERPs, internal CRMs, proprietary document management systems, and legacy databases. For tools without modern APIs, we can build a thin middleware layer.
What does it cost to run a RAG system?
+
Build costs typically range $15,000–75,000 depending on data volume and integration complexity. Monthly running costs depend on query volume — most clients spend $300–3,000/month on model inference plus $50–500/month on vector storage. Self-hosted options remove recurring AI costs but increase infrastructure overhead.
How do you handle data that changes frequently?
+
Every RAG system we build includes incremental sync — when a document changes in your wiki, document store, or database, we re-embed and re-index it within minutes. You can also trigger full re-indexing on demand. Retrieval always reflects current data, never a stale snapshot.
Can this be fully self-hosted for compliance reasons?
+
Yes. For HIPAA, GDPR, or sovereignty-sensitive workloads we deploy the entire stack inside your VPC or on-prem — open-source models, self-hosted vector databases, and no external API calls. Performance is typically 90–95% of managed-API quality on most tasks.