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AI Engineers

Hire AI Engineers

AI features that ship. Not science projects.

Our AI engineers build production LLM integrations, RAG pipelines, computer vision systems, and custom ML models. From feasibility assessment to deployed, monitored AI features.

Book a strategy callSee relevant workUpdated Mar 2026
40+

AI systems built

97%

Target accuracy met

8

Weeks to production

Overview

Why AI?

1Raft provides AI engineering teams that build production-ready AI features including LLM integrations, RAG systems, computer vision, and custom ML models. With 25+ AI products shipped across healthcare, fintech, and commerce, 1Raft engineers focus on measurable business outcomes - not research papers.

AI at 1Raft is an engineering discipline, not a research exercise. Our AI engineers build features that ship to production and generate measurable business value. We have deployed 25+ AI systems: conversational agents handling 10,000+ daily interactions, document processing pipelines saving 200+ hours weekly, and recommendation engines driving 15-25% revenue lifts.

We work across the AI stack: LLM integration with OpenAI, Anthropic, and open-source models; RAG systems with vector databases; computer vision with custom-trained models; and traditional ML for classification, prediction, and anomaly detection. The common thread is production-grade engineering with proper evaluation, monitoring, and guardrails.

Our approach starts with feasibility. Before writing code, we validate that AI is the right approach for your problem, define success metrics, and estimate infrastructure costs. This prevents the common pattern of spending months on an AI feature that could have been solved with a rules engine.

Why 1Raft for AI

  • LLM integration: OpenAI, Anthropic, Mistral, Llama
  • RAG pipelines: vector databases, embedding strategies, retrieval optimization
  • Fine-tuning and prompt engineering for domain-specific tasks
  • Computer vision: object detection, OCR, image classification

Expertise

What our AI team delivers

LLM integration: OpenAI, Anthropic, Mistral, Llama

RAG pipelines: vector databases, embedding strategies, retrieval optimization

Fine-tuning and prompt engineering for domain-specific tasks

Computer vision: object detection, OCR, image classification

MLOps: model versioning, A/B testing, drift detection

AI evaluation: benchmark suites, human-in-the-loop testing

Cost optimization: caching, model routing, token management

Guardrails: content filtering, hallucination detection, output validation

Process

How the engagement works

1
Week 1

Feasibility and cost modeling

Assess whether AI is the right approach. Define success metrics, estimate API/infrastructure costs, and identify data requirements. Kill bad ideas early before they consume budget.

2
Weeks 2-4

Prototype with real data

Build a working AI feature with your actual data. Measure accuracy, latency, and cost against the success criteria defined in week 1. Iterate on the approach based on results.

3
Weeks 5-9

Production engineering

Harden the prototype: add error handling, fallback logic, caching, monitoring, and guardrails. Build evaluation suites that run automatically on every deployment.

4
Weeks 10-12

Deploy and monitor

Production deployment with dashboards tracking accuracy, latency, cost, and user satisfaction. Runbooks for common failure modes. Model update pipeline for continuous improvement.

Use Cases

Real-world AI projects

AI document processing for a legal tech company

Built a RAG-powered system that extracts key clauses from contracts, summarizes documents, and answers natural language questions about legal agreements. Custom embedding model trained on legal corpus. → Contract review time reduced from 4 hours to 25 minutes. Extraction accuracy: 96.3% on key clauses. Processing 500+ contracts daily.

Conversational AI agent for customer support

Designed an AI agent using GPT-4 with RAG over the company knowledge base. Handles tier-1 support queries, escalates complex issues to humans, and learns from resolved tickets. → Resolving 68% of support queries without human intervention. Average response time: 8 seconds (down from 4 hours). CSAT maintained at 4.2/5.

Defect detection system for manufacturing

Computer vision system using custom-trained YOLO models to detect product defects on assembly lines. Real-time inference on edge devices with sub-50ms processing per frame. → Defect detection rate: 99.1% (up from 87% with manual inspection). False positive rate below 2%. ROI positive within 4 months.

Technology stack

PythonPyTorchLangChainOpenAI APIAnthropic APIPineconeWeaviateFastAPIDockerMLflowWeights & BiasesCUDA

Frequently asked questions

We start with a feasibility assessment in week 1. We evaluate whether the problem has enough data, whether the accuracy requirements are achievable, and whether the cost of AI is justified versus simpler approaches. About 20% of the time, we recommend a non-AI approach that achieves the goal faster and cheaper.

Next Step

Get a AI team matched to your product.

One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.

Talk to a founder
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