Intelligence · Production AI systems
AI Automation & Chatbots
Grounded assistants, RAG, workflow agents, and automation with guardrails for regulated industries.
Core delivery model
How Bits Bridge delivers
AI headlines outpace production readiness. Bits Bridge ships grounded assistants, evaluation harnesses, and workflow automation with human oversight — integrated to CRM, LMS, and policy data.
Delivered AI programs, platform depth (RAG, evals, governance), and pilot engagement paths.
Regulated clients need citations, audit logs, and escalation — not unconstrained generative chat on customer-facing channels.
Engagements can start with paid discovery or a fixed-scope MVP — whichever fits your procurement process. We align with existing vendors and internal squads when RACI boundaries are clear.
What we have delivered
We ship production AI — RAG tutors scoped to enrolled learners, insurance FAQ assistants with citations, FNOL triage summarization, and ops autopilot with human-in-the-loop approvals — not demo chatbots.
Many clients extend existing Rails portals with AI layers — retrieval, summarization, and workflow agents — rather than greenfield chatbot products.
- Enrollment-aware RAG for EdTech — private course content never crosses tenants
- Insurance and telecom FAQ bots with mandatory escalation on binding advice
- Document classification and OCR pipelines feeding human review queues
- Agent tools: CRM note summarization, ticket routing, and suggested replies
- Evaluation harnesses: golden datasets, red-team prompts, latency/cost benchmarks
- Workflow automation connecting LLM outputs to governed APIs (create case, draft email)
Technical capabilities
AI delivery requires retrieval grounding, access control, observability, and compliance review — we integrate OpenAI, Anthropic, Gemini, and open models via unified adapter layers.
Model routing sends classification to smaller models and generation to premium models — controlling cost without sacrificing quality on critical paths.
- Vector indexes (pgvector, Pinecone, OpenSearch) with metadata filters for RBAC
- Prompt versioning, A/B evaluation, and rollback when quality regresses
- PII redaction, audit logs, and retention policies aligned to your legal guidance
- Streaming UX with timeout fallbacks and graceful degradation
- Cost dashboards per tenant, model, and feature flag
- Human-in-the-loop UI for approve/edit/reject before customer-facing send
What we can build for you
Start with one high-volume, low-risk use case — FAQ deflection, internal summarization, or document triage — in a 6–8 week pilot with evaluation metrics.
Red-team sessions test jailbreak and PII leakage scenarios before customer-facing launch — findings remediated or mitigated with escalation rules.
- Discovery: data sources, compliance boundaries, success metrics, failure modes
- Corpus preparation, chunking strategy, and retrieval evaluation before UI polish
- Pilot on staging with red-team testing and ops containment rules
- Production rollout with feature flags and monitoring on hallucination rate
- Optional expansion to workflow agents and multi-channel deployment
- Learn more about WhatsApp bots and custom Rails platforms
Stack
Technology stack
Production AI combines vector retrieval, prompt versioning, Rails/Node orchestration, Python workers for batch eval, Redis queues, and CloudWatch cost/latency monitoring.
Secrets live in AWS Secrets Manager — never in repos. Testing includes contract tests on partner APIs and load tests before predictable peak events.
Outcomes
Delivery highlights
Golden set accuracy, containment with CSAT, and cost per conversation — tracked from pilot through production rollout.
KPIs are agreed at discovery and reviewed at 30/60/90 days post-launch — conversion, deflection, deploy frequency, or decision clarity for research engagements.
Grounded answers
Citation-backed RAG — Responses link to approved sources — critical for regulated FAQ and EdTech tutoring.
Safety
Human escalation paths — Binding advice, claims decisions, and billing disputes route to licensed staff with logs.
Evaluation
Golden test sets — Automated regression on intent accuracy and retrieval precision before each release.
Cost control
Model routing & caching — Smaller models for classification; premium models for generation — with spend dashboards.
Depth
Technical capabilities
FAQ bots, copilots, document triage, summarization, and governed workflow agents across web, WhatsApp, and internal tools.
Handover includes documentation your team can operate: runbooks, API references, and recorded training — not a final-week knowledge dump.
Use cases
- Customer FAQ bots
- Internal copilots
- Document triage
- Summarization
- Workflow agents
Platform
- Retrieval pipelines
- Prompt management
- Evaluation harnesses
- Feature flags
- Audit & compliance logs
Channels
- Web widgets
- Agent desktop
- Mobile apps
- Slack/Teams internal bots
Next steps
What we can do for you
Engagement models
Share the journey, data sources, and compliance constraints. We propose a pilot with measurable deflection or productivity KPIs — not open-ended experimentation.
- AI pilot (6–8 weeks)
- Multi-use-case rollout
- Retained AI engineering & eval ops
How we deliver
Weekly demos, written decision logs, and KPI reviews at 30/60/90 days — delivery craft since 2013.
- Discovery workshop — stack inventory, KPIs, compliance constraints
- Architecture & integration contracts — OpenAPI, webhook catalog, RACI
- Iterative build with weekly demos on staging
- Load tests, security review, and rollback runbooks
- Hypercare launch + optional retainer for upgrades
Share your stack, timeline, and the KPI you need to move.
Contact form →Questions
Frequently asked questions
Do you build generic ChatGPT wrappers?
No. We build grounded systems with retrieval, access control, evaluation, and escalation — integrated to your CRM, LMS, or policy data.
Which LLM providers do you support?
OpenAI, Anthropic, Google Gemini, and open models via adapter layers — chosen for latency, cost, and data residency requirements.
How do you prevent hallucinations in regulated industries?
Retrieval grounding, citation requirements, confidence thresholds, and mandatory human review on customer-facing output in sensitive domains.
Can AI respect tenant isolation in SaaS?
Yes. Metadata filters on vector indexes enforce enrollment and role boundaries — validated before multi-tenant scale.
What metrics do you track for AI pilots?
Containment with CSAT, answer accuracy on golden sets, escalation rate, latency p95, and cost per conversation.
Do you help with prompt governance?
Yes. Versioned prompts, approval workflow, and rollback when evaluation scores drop post-change.
Can AI integrate with WhatsApp?
Yes. RAG-backed FAQ within session windows, with template fallbacks when sessions expire.
How do we start?
Email hello@bitsbridge.in with use case, data sources, and compliance contacts. We respond with a pilot scope and evaluation plan.