Add AI to Any Product. In Weeks, Not Months.
We integrate GPT-4, Claude, Gemini, and open-source LLMs into your existing software — building production-grade AI features that are reliable, cost-controlled, and grounded in your data.
Your Existing Product
REST API · Webhook · SDK
AI Middleware Layer
RAG · prompt engineering · guardrails
LLM Provider
GPT-4o · Claude · Gemini · Llama
20+
AI Integrations Shipped
<80ms
Avg. API Response Time
99.9%
Integration Uptime SLA
8+
LLM Providers Integrated
87% of AI Demos Fail in Production
They worked perfectly in the demo. They crash under real load. Here's why — and how we prevent it.
What actually changes between Week 1 and Month 3
Week 1 — The Demo
Month 3 — Production Reality
87%
Of AI demos don't survive 6 months in production
Root cause: missing infrastructure — not model quality
4 wks
To production-ready with Ethersofts
Includes fallbacks, caching, observability, and cost controls from day one
The Right LLM for Every Use Case
We benchmark models on your specific task — not generic leaderboards. Here's what each is actually good at.
Best reasoning & tool use
GPT-4o
Best for: Complex reasoning, code generation, structured data extraction, multi-step function calling
Key Strengths
Context
128K tokens
Cost
$2.50–$10 / 1M tokens
Latency
~800ms avg
96.4%
Function call accuracy
GPT-4o on ToolBench eval
# GPT-4o function calling trace
user → "Extract all payment terms from contract_012.pdf"
thinking → needs extract_clauses + parse_dates tools
tool_call extract_clauses(doc="contract_012", type="payment")
← ["Net 30 from invoice", "Late fee 1.5%/month"]
tool_call parse_dates("Net 30 from invoice")
← { due_days: 30, from: "invoice_date" }
response { net_days: 30, late_fee_pct: 1.5 }
↓ latency: 847ms · tokens: 312 · cost: $0.003
We Connect Your Stack to Every AI Platform
60+ integrations shipped. Whichever tools you run, we've already connected them to AI.
LLM Providers
Business Systems
Vector Databases
Frameworks
How We Ship AI That Works in Production
Most AI integrations fail because they skip the unglamorous steps — fallback chains, eval suites, prompt versioning. We don't. Every engagement follows the same four-phase playbook.
Observability from day one
Latency, cost, accuracy tracked per deployment
RAG-first architecture
Answers grounded in your data, never hallucinated
Fallback chains built in
Primary model down? Secondary kicks in automatically
Use Case Design
We map the AI workflow: inputs, outputs, required tools, and fallback paths — before writing a line of code.
Outcome: signed-off architecture doc + edge case inventory
Prompt Engineering
We craft and iterate prompts using structured outputs, few-shot examples, and chain-of-thought to maximize accuracy.
Outcome: eval suite with baseline accuracy benchmarks
Build & Connect
We build the integration middleware, connect APIs, and wire up retrieval layers, vector databases, and business systems.
Outcome: staging environment with full observability
Monitor & Optimize
We set up evaluation pipelines, cost tracking, latency monitoring, and continuous prompt improvement loops.
Outcome: production handoff with runbook + cost dashboard
Production AI vs. Basic API Wrapper
A basic API wrapper works in demos. It silently fails your users when it counts.
| Feature | Production AI Integration | Basic API Wrapper |
|---|---|---|
| Error handling | Graceful fallbacks + retry logic | |
| Rate limit protection | Queued requests + burst buffers | |
| Cost controls | Per-user budgets + alerts dashboard | |
| Observability | Prompt traces, latency, cost per call | |
| Prompt versioning | A/B tested, rollback in < 60s | |
| RAG / grounding | Answers from your actual data | |
| Data privacy | On-prem option, zero training data |
The Challenge
Legal tech startup's paralegals spending 2+ hours reviewing each contract — limiting throughput to 4 contracts/day per person and creating a serious backlog.
What We Built
Claude-based document analyzer integrated directly into their existing platform. RAG pipeline indexed against contract templates and regulatory guidelines.
The Result
2 hrs → 8 sec
Contract review time
3×
Team throughput, same headcount
4 weeks
Time from kickoff to production
Ready to add AI to your product? Talk to an engineer this week — not a sales rep.
“We added AI document analysis to our legal platform in 4 weeks. What used to take a paralegal 2 hours now takes 8 seconds — and the AI is more consistent. It's completely transformed our throughput.”
Questions we get all the time
If yours is not here, reach out. We respond within 24 hours with a real answer from an engineer — not a sales pitch.
It depends on your use case, budget, and data privacy requirements. We benchmark multiple models on your specific task before recommending. GPT-4o excels at reasoning and code; Claude shines on long documents and nuanced writing; Gemini integrates natively with Google Workspace.
Yes. We offer two approaches: (1) use enterprise API tiers where data isn't used for training (available from both OpenAI and Anthropic), or (2) deploy open-source models (Llama 3, Mistral) on your own cloud or on-prem infrastructure. Nothing leaves your environment.
We implement multiple guardrails: retrieval-augmented generation (RAG) to ground answers in your data, structured output parsing to constrain model responses, confidence scoring, and human-in-the-loop checkpoints for high-stakes decisions.
A focused LLM integration (e.g. AI search in your app, document summarization, email categorization) typically takes 3–6 weeks. A full AI agent with multi-step planning, tool use, and custom RAG takes 8–14 weeks. We start with a scoping call.
OpenAI API costs scale with usage and can run into thousands per month at high volume. A self-hosted Llama 3 on a GPU server has a fixed infrastructure cost (typically $300–1500/month) with unlimited inference. We model both scenarios for your expected volume.
Yes — that's the core of what we do. We add an AI middleware layer that connects to your product over REST, webhooks, or SDKs, so your existing codebase and database stay untouched. We've integrated LLM features into Node.js, Python, Java, .NET, and Go backends, plus Salesforce, HubSpot, Zendesk, and custom platforms.
RAG (retrieval-augmented generation) embeds your documents into a vector database (Pinecone, Weaviate, Qdrant, or pgvector) and feeds the most relevant passages to the model at query time, so answers are grounded in your actual data instead of hallucinated. If your use case involves answering from internal docs, policies, or product knowledge — most do — then yes, RAG is essential, and we build it into nearly every integration.
Your Product, Supercharged With AI
Don't rebuild from scratch. Let Ethersofts add powerful AI features to your existing product — in weeks, not quarters.
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