AI-native features for software companies that need to ship them right
We help product teams add copilots, search, generation and automation to their SaaS with the evaluation, cost controls and reliability that customers expect from a paid product.
Where AI pays off in SaaS & Technology
In-product copilots
Context-aware assistants that answer, draft and act inside your application.
Semantic search & RAG
Search and Q&A over customer data with permissions and citations.
Platform reliability
Evaluation, routing, caching and observability for LLM features at scale.
SaaS & Technology at a glance
How we help
Problems we solve in SaaS & Technology
Each maps to a reference architecture and to outcomes the industry has documented.
Support queues that never shrink
Tier-1 questions consume most of the team's day, answers vary by agent, and customers wait. Generic chatbots deflect badly because they do not know your policies or your systems.
How the system works
- Customer message
- Intent & policy check
- Retrieve from your docs & orders
- Answer or act via tools
- Escalate with context
Outcome: Klarna's assistant handled two thirds of customer chats in its first month and cut resolution time from 11 minutes to under two; Intercom reports Fin resolving 76% of conversations on average. Klarna press release
ArchitectureAnswers buried in ten systems
Policies live in SharePoint, decisions in email, procedures in Confluence and history in tickets. New staff take months to become useful and experts answer the same questions daily.
How the system works
- Connect sources
- Chunk with context, embed + index
- Hybrid retrieval + rerank
- Cited answer with permissions
- Feedback into evals
Outcome: Contextual chunking with hybrid search and reranking cuts retrieval failures by 67% versus plain vector search, which is the difference between an assistant people trust and one they abandon. Anthropic: Contextual Retrieval
ArchitectureEvery question needs an analyst
Leaders wait days for a report, dashboards disagree with each other, and the data team is a queue. Raw data sits in the warehouse without becoming a decision.
How the system works
- Raw sources
- Lakehouse with partitions & quality checks
- Semantic layer of metrics
- Text-to-SQL agent
- Dashboards & alerts
Outcome: In dbt's own test, questions answered through a governed semantic layer reached 83% accuracy, against a 16.7% raw-SQL baseline in the data.world benchmark it replicated; vendors report time-to-insight falling by over 99% with 62% adoption within a year. dbt: the semantic layer as the data interface for LLMs
ArchitectureLet's build intelligent systems that drive growth
Tachyon is the engineering partner for teams that need AI in production, not in a deck. Start with a free 60-minute discovery call.
Talk to experts about your product idea
Every great partnership begins with a conversation. Whether you are exploring possibilities or ready to scale, tell us what you are actually trying to build.
- ubheshubham.37@gmail.com
- +91 84592 96471
- Clients worldwide · English
- Pune, India · Headquarters