[ A separate tab is where AI features go to die ]
An AI capability that lives in a separate tab creates a new place for work to go missing. The value appears when it operates inside the CRM your sales team already has open, the ERP your finance team lives in, or the helpdesk your agents never leave. Techtaru Digital is an AI integration company that connects LLMs, agents and chatbots into existing business systems — including the legacy ones without modern APIs.
An AI capability that lives in a separate tab creates a new place for work to go missing. The value appears when it operates inside the CRM your sales team already has open, the ERP your finance team lives in, or the helpdesk your agents never leave. Techtaru Digital is an AI integration company that connects LLMs, agents and chatbots into existing business systems — including the legacy ones without modern APIs.
Tell us where your team currently copies and pastes between toolsconnecting AI capability into your specific systems and workflows.
provider APIs wired into your applications with retries, fallbacks and cost controls.
model access standardised across teams with governance and routing.
GenAI features embedded into existing products and internal tools.
connecting agents to the tools and systems they need to act on.
assistants embedded into helpdesks, CRMs and messaging channels.
retrieval connected to live document stores and databases rather than a static export.
AI capability added where no modern API exists.
architecture, provider selection, cost modelling and governance design before any code.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
OpenAI and GPT integration, Claude integration, Gemini integration, Llama and open-weight model integration — implemented behind an abstraction layer so switching provider is a configuration change rather than a rebuild. Includes streaming, retries, rate-limit handling, fallback routing and token accounting.
Salesforce, HubSpot, Zoho and Dynamics — call summarisation, next-best-action suggestions, automated enrichment, email drafting and pipeline hygiene, surfaced inside the CRM record rather than beside it.
SAP, Microsoft Dynamics, NetSuite, Odoo and Tally — invoice extraction and matching, purchase order processing, exception triage and natural-language reporting over ERP data.
Chatbots embedded in Zendesk, Freshdesk, Intercom and Salesforce Service Cloud, plus WhatsApp AI chatbot integration and website chatbot integration, with full conversation context passed on escalation.
Connecting agents to your tools through function calling or MCP servers, with scoped credentials per tool, approval gates on consequential actions and complete call logging. We also build the agents that act across systems themselves.
Retrieval wired to SharePoint, Confluence, Google Drive, Notion, S3 and databases, with incremental sync so answers reflect current content and permission-aware retrieval so people only see what they may.
Slack and Teams assistants, email and calendar integration, document management, ITSM and HR systems — AI placed where the work already is.
Where systems predate APIs: database-level integration, file-based exchange, middleware layers, screen-scraping or RPA bridges where nothing else exists, and API facades built over legacy cores so future integration is cheaper.
A shared internal layer providing model access, prompt management, cost attribution by team, guardrails, caching and audit logging — so every department is not integrating independently and invisibly.
what exists, what is documented, what is actually accessible, and where the legacy constraints sit.
gateway design, provider routing, data flow mapping, PII handling and cost model.
one workflow, one system, instrumented for usage and cost, measured on adoption rather than capability.
retries, fallbacks, caching, rate-limit handling, monitoring, then extension to further systems.
audit logging, cost dashboards, documentation and runbooks your team can operate.
Model providers: OpenAI, Anthropic, Google, Meta Llama, Mistral, plus Azure OpenAI, AWS Bedrock and GCP Vertex for enterprise deployment. Abstraction: LiteLLM or a bespoke gateway with routing, caching and cost attribution. Tooling: Model Context Protocol servers, function calling, webhook infrastructure. Integration: REST, GraphQL, SOAP for legacy, message queues, iPaaS platforms where they already exist. Retrieval: pgvector, Pinecone, Qdrant with connectors to SharePoint, Confluence, Drive and S3. Identity: SSO via SAML or OIDC, scoped service credentials, secrets vaulting. Observability: request tracing, token and cost dashboards, error alerting.
Abstract the provider from day one. Teams that hard-code one vendor's SDK across their application discover the cost when pricing changes, a better model ships or an outage takes them down. A thin gateway layer — routing, retries, fallback, caching, cost tracking — takes days to build and saves a rebuild later.
Integrate into the workflow, not alongside it. An AI feature requiring someone to leave their CRM, open another tool, copy context in and paste output back will not be used after week two. Adoption tracks proximity to where work already happens more than it tracks output quality.
Cost attribution matters more than people expect. Without per-team and per-feature token tracking, AI spend becomes one unexplainable line item and the first budget review kills initiatives indiscriminately. We instrument attribution from the first integration.
Rate limits and failures are design inputs. Provider APIs throttle, time out and occasionally go down. Production integrations need exponential backoff, queueing for non-interactive work, fallback to a secondary provider, and graceful degradation that tells the user something honest.
Caching is the cheapest optimisation available. Semantic caching of repeated queries and prompt caching of long shared context routinely cut inference spend substantially in support and internal-knowledge use cases, where the same questions recur constantly.
Legacy integration is usually possible, and the path matters. In order of preference: existing API, database-level integration with a read replica, file-based exchange, then RPA as a last resort. We also build an API facade over the legacy core where the integration will be long-lived, because every subsequent project then becomes cheaper.
Governance travels with the integration. Which data reaches which provider, retention, PII redaction before transmission, and audit logs of AI-assisted decisions. Frameworks like the NIST AI RMF give this structure, and the EU AI Act places obligations on deployers.
Fixed scope for defined integrations; phased delivery for enterprise integration platforms; monthly squads where integration is continuous; retainers for monitoring and cost optimisation.
Indicative cost: a single system integration — AI into one CRM or helpdesk workflow — typically runs $6,000–$20,000. Multi-system enterprise integration programmes generally land at $25,000–$70,000. An enterprise AI gateway with governance, cost attribution and multi-team access is larger and phased. Legacy integration is quoted after the API inventory, because the access path drives the cost far more than the AI side does.
The integration that proves value is a single workflow in a single system, instrumented for adoption and cost. Tell us where your team currently copies and pastes between tools, and we will scope that first.
A single system integration typically runs $6,000–$20,000; multi-system programmes $25,000–$70,000. Legacy integration is quoted after an API inventory, since access path drives cost.
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