Support automation
Agents resolve tickets with CRM and order lookup, human handoff when confidence is low.
Service
AI Solutions
Production AI, LLMs, agents, RAG, and automation that ship beyond the demo.
71%
Tickets auto-resolved without human
50%
AI cost reduction via smart routing
8 weeks
Typical agent MVP timeline
Problem statement
Why teams struggle without AI Solutions
We integrate OpenAI, Claude, Gemini, and open-source models into products users rely on daily, chatbots, document automation, recommendation engines, and multi-step agents with evaluation loops and cost controls. No slide-deck AI; only systems instrumented, versioned, and ready for real traffic.
01
Demo-to-production gap
Prototype prompts break on edge cases, hallucinate, and cost unpredictably at scale.
02
Data privacy concerns
Sensitive documents and customer data require careful routing, retention, and access policies.
03
Model vendor lock-in
Hard-coding one provider makes upgrades expensive and risky when better models ship quarterly.
How we help
Key focus areas
- 1RAG and tool-use architecture
Retrieval pipelines, function calling, and policy layers keep answers grounded and auditable.
- 2Evaluation harnesses
Golden datasets and regression tests block merges when accuracy, latency, or cost slip.
- 3Model abstraction
Swap GPT, Claude, or open-source backends without rewriting business logic.
Built for
Who gets the most from this service
Product teams adding AI features to existing platforms
Enterprises automating document-heavy workflows
Startups building AI-native SaaS products
Ops leaders replacing manual triage with intelligent agents
Capabilities & stack
Core capabilities & execution scope
Practical delivery scope for AI Solutions — not a generic feature dump.
What we cover
- AI Chatbots
- LLM Integration
- OpenAI Integration
- Claude Integration
- Gemini Integration
- AI Agents
- Business Automation
- AI Workflows
- RAG Systems
- Prompt Engineering
Tools & architecture
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Schedule a technical review Business impact
Why this service matters
71%
Tickets auto-resolved without human
50%
AI cost reduction via smart routing
8 weeks
Typical agent MVP timeline
Our approach
We combine discovery workshops with senior engineering so every decision is tied to a measurable outcome for AI Solutions.
- Scope: Use case selection, data audit, and success metrics definition.
- Prototype: Rapid POC with real data and stakeholder validation.
- Productionize: Evals, guardrails, observability, and gradual rollout.
What you get
Tangible artifacts and a clear handover — not vague “strategy decks.”
- 1Production AI feature or standalone application
- 2Vector store and ingestion pipeline documentation
- 3Eval suite with baseline accuracy metrics
- 4Cost and latency monitoring dashboard
- 5Security review and data handling policy
Delivery framework
A structured 4-step path
Clear checkpoints and review gates keep AI Solutions transparent from kickoff to launch.
1
Scope
Use case selection, data audit, and success metrics definition.
2
Prototype
Rapid POC with real data and stakeholder validation.
3
Productionize
Evals, guardrails, observability, and gradual rollout.
4
Optimize
Prompt tuning, caching, and model routing for cost efficiency.
Use cases
Where AI Solutions creates impact
Real scenarios where this service produces measurable business results.
Document intelligence
Extract, classify, and summarize contracts, invoices, and medical records at scale.
Product copilots
In-app assistants that draft content, analyze data, and guide users through complex workflows.
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WeaviateHugging FacePyTorchTensorFlowPythonFastAPIn8nWeaviateHugging FacePyTorchTensorFlowPythonFastAPIn8nWeaviateHugging FacePyTorchTensorFlowPythonFastAPIn8n
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