We integrate GPT-4, Claude, embeddings, RAG, and agents directly into your existing apps, CRMs, and workflows — without rebuilding what already works.
30 minutes · Senior engineer · No commitment
Beyond simple API calls — we build production-grade AI features with proper guardrails.
OpenAI, Anthropic, Google, Mistral, and open-source models with smart routing and fallbacks.
Retrieval-augmented generation over your docs with vector DBs (Pinecone, pgvector, Qdrant).
Multi-step agents that use tools, call APIs, and complete real workflows — not just chat.
Prompt injection defense, PII redaction, output validation, and compliance-friendly logging.
Token streaming, response caching, and parallel calls for snappy real-time UX.
Smart prompt design, caching, and model selection that cuts your AI costs by 40–70%.
Where AI delivers immediate, measurable value to existing products.
Semantic search and Q&A over your help center, knowledge base, or product documentation.
Auto-summarize calls, draft follow-up emails, score leads, and surface next-best-action.
Extract structured data from invoices, contracts, and PDFs with validation and human-in-the-loop.
Branded blog posts, product descriptions, social copy, and translations at scale with QA gates.
Chatbots, workflows, and CRM don't share context — customers repeat themselves.
AI features work in staging but fail under real load or API changes.
Automations break when integrations update unless someone monitors them.
n8n, GPT-4, ElevenLabs, Retell — designed as one system, not siloed demos.
Ad Grants Pilot AI admin panel, Shepherd AI tutor app, and production chatbot deployments.
Alerting and tuning after go-live — we don't disappear after handoff.
“What a 70-engineer team could not deliver, a small senior team at GreeLogix shipped. The app went from stuck to live across mobile, web, and backend.”
At-a-glance scope, pricing tiers, delivery timelines, engineering tradeoffs, and decision criteria for technical founders and product leaders.
AI automation combines workflow tools (n8n), large language models (GPT-4, Claude), and optional voice or phone agents to handle repetitive business tasks — support, lead qualification, scheduling, and data sync between systems.
Pricing depends on scope, integrations, and timeline. GreeLogix provides transparent fixed-milestone quotes after discovery, with flexible models for one-off audits, sprint retainers, or dedicated engineers.
Simple workflows: 1–2 weeks. Chatbots with RAG: 3–4 weeks. Voice or phone agents with CRM: 3–6 weeks. Phases: Discovery (Week 1); Build (Week 2–4); Launch (Week 4+).
Compare delivery models, cost profiles, and engineering tradeoffs before committing resources.
| Option / Approach | Best When | Primary Tradeoff / Risk | Recommended Fit |
|---|---|---|---|
| Zapier / Make DIY | Simple 2-step automations with no custom logic or AI | Scalability bottlenecks, vendor lock-in, architectural rigidity, and substantial technical debt before handling production traffic. | Early validation & UI experiments |
| Off-the-shelf chatbot SaaS | Generic FAQ widget when you don't need CRM writes or custom flows | Recurring per-seat software fees, inflexible workflow constraints, and zero proprietary software or IP ownership. | Standard non-differentiating processes |
| In-house AI team | You already have ML engineers and want full control of model training | 3–6 month hiring cycle, high recruitment fees, and fixed long-term payroll overhead regardless of development volume. | Companies with dedicated HR pods |
| GreeLogix Pod | You need production AI workflows and voice agents hooked directly into your real CRM and database, not just isolated demos. | Requires active product owner for weekly sprint reviews and scope alignment. | Production & Scaling (Recommended) |
Talk to a senior engineer — scope, timeline, and cost range in one 30-minute call. No sales script.
Answers to the buyer questions we hear most before a project starts.
We integrate OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Google (Gemini), Mistral, and open-source models like Llama. We also build custom fine-tuned models for specialized use cases.
RAG (Retrieval-Augmented Generation) lets AI answer questions using your own documents and data. Yes, we build RAG pipelines using vector databases like Pinecone, pgvector, and Qdrant.
We map where AI delivers ROI in your business and prioritize the highest-value integrations.
Working proof-of-concept in 1–2 weeks so you can validate quality before committing.
Integrate into your stack with monitoring, evals, guardrails, and proper error handling.
Continuous prompt tuning, cost optimization, and feature expansion based on real usage.
Tell us what you're building and we'll suggest the right AI integration plan.
From chatbots and voice agents to workflow automation — pick the capability that matches your roadmap.
Use our free calculators and planners — then book a strategy call when you are ready for a senior engineer review.
Case studies, guides, and free tools from the same engineering team.
Scope, timeline, and cost range — no sales deck. Or start with the free readiness quiz if you are still evaluating your stack.
You'll talk to Abuzer — the senior engineer who'll scope your build
Founder & Senior Engineer · Response within 4 business hours
Book a Free 30-Min CallResponse within 4 business hours · No commitment on first call