Vetted AI developers who ship LLM apps, RAG pipelines, multi-agent flows, and Anthropic Claude / OpenAI / Gemini integrations into production. Backed by the team behind the GetWidget open-source Flutter UI kit. 48-hour AI developer match. 30-day replacement guarantee.
We staff AI developers, not ML engineers. The distinction matters because hiring the wrong role wastes a quarter of payroll. Use this section before you write the JD. Most "AI app development" work in 2026 is integration: take a frontier model API, ground it on the client's data, ship an AI powered mobile app. That's an AI developer's job. As an AI development company India clients trust, we field this question weekly — clients who think they need an ML team usually need two integration developers and a vendor model.
Application engineer. Ships LLM features, RAG, agents, AI mobile apps using foundation models from Anthropic, OpenAI, Google, or open-source via Bedrock / Together. Owns prompt design, eval coverage, cost guardrails, observability, safety patterns.
Trains and deploys models. Owns distributed training, fine-tuning runs, MLOps infrastructure, GPU clusters, model registries. If your AI feature needs a custom model trained on your data from scratch, you need an ML engineer plus an ML scientist — we'll tell you that on the discovery call and won't try to staff it.
The honest rule: 80% of "AI features" companies want in 2026 are AI-developer work — integrate Claude or GPT, ground it on your docs via RAG, add observability and cost caps, ship it. The remaining 20% need model training, and we'll point you elsewhere when that's the case.
AI developer rates run higher than standard Flutter rates because the market for senior AI engineers is hotter. Our pricing still lands well under US-based AI dev rates ($150-$280/hr) at the same delivery quality.
API integration, prompt tuning, eval harness setup, basic RAG glue. Always reviewed by a Senior on every PR.
Owns LLM features end-to-end: prompt design, eval coverage, cost guardrails, vector DB integration, observability.
Architecture decisions, multi-agent flows, RAG patterns, eval methodology, on-device ML, safety + guardrails.
Owns pod direction, hiring, vendor selection (Anthropic vs OpenAI vs Gemini), customer demos, shipping calendar.
Inference cost (Claude / OpenAI / Gemini API spend) is billed pass-through with no markup. Vector DB hosting (Pinecone, Weaviate, pgvector on Supabase) is pass-through too. Monthly rolling contracts after the first 30 days.
We don't sell "AI consulting". We ship working AI features that pass an eval harness and stay within a cost budget. These six categories cover ~90% of what companies actually want from AI development in 2026.
In-app chat, conversational onboarding, content generation, intelligent search, document Q&A. Claude, GPT-4 family, Gemini Pro.
Embedding + chunking + retrieval + reranking + grounded generation. pgvector, Pinecone, Weaviate, Chroma. Evaluation with Ragas.
LangGraph or custom orchestration. Tool-use, agent memory, human-in-the-loop gates. Anthropic computer use, OpenAI Assistants.
AI mobile app development for iOS + Android via Flutter or React Native. On-device ML with Core ML, TensorFlow Lite, or cloud-routed inference.
Prompt injection defence, PII scrubbing, output filtering, refusal handling. Red-team eval suites. SOC 2 + GDPR + HIPAA scope.
LangSmith, Langfuse, Braintrust, Inspect AI. Cost dashboards, latency budgets, hallucination rate tracking.
The most-asked question on our discovery calls. Honest answer: not "prompts ChatGPT all day". Every ticket follows a spec-to-eval-gated-PR loop. Day looks like this.
15-minute async-friendly standup over Slack and Loom. Sync with your US East / UK / UAE window from 09:30 IST. Blocking decisions surface here; nothing waits 24 hours.
Open the ticket in Claude Code. Draft the agent flow as a LangGraph DAG, list the tools, name the eval set, decide the model (Claude Sonnet / Gemini Flash / GPT-4o) and the cost ceiling per request. Output: a written spec before any code lands.
10-30 input/output pairs in Braintrust or LangSmith, scored by an LLM judge plus regex checks for the deterministic parts. Eval runs in CI on every PR. No feature ships without one.
Implementation runs against the spec. Prompts go in a versioned prompt library, not hardcoded strings. Iterate prompt + model + RAG retrieval until eval score clears the threshold the spec named.
Add cost guardrails (per-user / per-org daily caps). Run prompt-injection eval set. Run PII-leak eval set. Add Langfuse observability so production hallucination rate and p95 latency are visible from day one.
Diff lands in your repo. PR description shows eval score before / after, cost delta, latency delta. Senior pair-reviewer signs off on prompt design + safety. Your lead reviews business logic. Two-pass review, AI never replaces it.
The vetting rubric we use ourselves. Bookmark this if you're running your own AI developer interviews. Every developer on our bench passes all eight before placement.
Knows how to write a system prompt that's testable. Uses XML or markdown structure for Claude, function-calling schemas for OpenAI. Versions prompts in source control. Doesn't tweak prompts blind in a UI.
Knows when to chunk by token vs by section, when to add a reranker (Cohere, Voyage), when hybrid search beats pure-vector, when to skip RAG entirely and stuff the doc in context. Has shipped at least one production RAG pipeline.
Writes evals before features. Comfortable with LLM-as-judge plus deterministic checks. Knows the difference between offline eval, online eval, and red-team eval and ships all three for safety-critical features.
Has worked with at least two of pgvector, Pinecone, Weaviate, Chroma, Qdrant. Knows the index trade-offs (HNSW vs IVF), the metadata-filter cost, and the chunking-vs- recall trade for the specific use case.
Builds in LangGraph or custom orchestration. Knows when an agent loop is the right answer and when a single LLM call with structured output is cheaper, faster, and more reliable. Defaults to the boring choice.
Wires Langfuse or Helicone before shipping. Knows the per-user cost of every LLM call. Sets cost ceilings per org. Has rescued at least one project from a $20K surprise OpenAI bill.
Runs prompt-injection eval sets. Strips PII before logging. Implements refusal handling that doesn't degrade the legitimate use case. Knows the SOC 2, GDPR, and HIPAA scope of LLM data flows.
Non-negotiable for our placements. Features ship 2.3× faster than non-AI peers (2026-Q1 internal data). Means a Senior AI dev outputs what a Lead used to.
The hiring funnel that staffs your engagement is the same one we use for our own AI mobile app builds. You see the survivors, not the funnel.
Map the AI use case, current stack, model preference (Claude / OpenAI / Gemini / open-source), data sensitivity, eval needs, timezone overlap. Output is a written role brief.
Shortlist 2-3 vetted AI developers whose stack matches. You meet each on a 60-minute pairing call. Candidates demo a real AI feature they've shipped.
Selected developer joins your repo. First PR usually inside week one. By end of week two, the eval harness is running and cost guardrails are in place.
If chemistry or skill match isn't right inside 30 days, we replace at no cost. Most mismatches surface inside the trial week and we swap by day five.
"AI developer" can mean anything in 2026, so here's the specific stack. We use this in production, not in a slide deck.
Anthropic Claude (Sonnet / Haiku / Opus), OpenAI GPT-4 family + Realtime + Assistants, Google Gemini Pro, Llama / Mistral / Qwen via Bedrock or Together, Hugging Face hosted endpoints.
LangGraph for multi-agent flows. LangChain + LlamaIndex for RAG patterns. FastAPI / Temporal for production orchestration. Inspect AI for eval. Custom orchestration when frameworks add more than they save.
pgvector on Supabase or RDS Postgres. Pinecone, Weaviate, Chroma. Cohere Rerank for hybrid retrieval. BM25 for keyword baselines. Unstructured.io for document ingest.
Ragas for RAG eval. LangSmith + Langfuse for trace + eval. Braintrust for production eval workflows. Inspect AI for red-team safety. Custom cost + latency dashboards.
Authority anchors: Anthropic · OpenAI · Google AI · Hugging Face
Each engagement below is real, anonymised by industry. Numbers come from the production eval harness or the client's reported metric. We can walk through the relevant one on the discovery call.
HIPAA-scoped intake assistant. Claude Sonnet + private endpoint, signed BAA. Reduced triage handle-time 38% (2026-Q1).
RAG over policy docs + ticket history. Pinecone + Cohere Rerank + Claude. 47% tier-1 deflection (2026-Q1).
Multi-step contract-clause extractor. LangGraph + Claude Opus. Replaced 6 hours of paralegal review per contract with 12 minutes.
Flutter mobile app with embedded Gemini Pro recommendation engine. 22% AOV lift on the assisted-checkout cohort (2026-Q1).
Slack-native RAG agent over Confluence + Notion. pgvector + Claude Haiku. 1,200 employees, 94% answer accuracy on internal eval set (2026-Q1).
Replaced ElasticSearch keyword with embedding + reranker. Weaviate + Cohere. 31% lift in search-to-conversion rate.
We've shipped 1,000+ Flutter projects and a growing number of AI-native applications. Our developers learned Flutter and Dart by maintaining the GetWidget UI kit on pub.dev — and the same engineering rigour ships through to our AI work.
