Multimodal AI Development Services

For a focused scope — say, a vision-language document classifier or an image-grounded search feature — plan on 12 to 16 weeks from kickoff to a production-serving endpoint. The first four weeks are discovery and data preparation, weeks five through ten cover model development and iterative fine-tuning, and the final phase is hardening, load testing, and handoff documentation. Greenfield projects with clean labeled data land at the shorter end; projects that need a labeling pipeline built from scratch add four to six weeks.

Codieshub engagements for multimodal AI typically range from $80,000 to $250,000 depending on model complexity, data volume, and required inference SLA. A lean two-engineer team on a focused image-text classification project is at the lower end. A full-stack engagement with custom training infrastructure, a labeling pipeline, and a production inference cluster runs higher. We scope fixed-price milestones after discovery, so cost doesn't drift.

Not necessarily. Parameter-efficient fine-tuning methods like LoRA can adapt foundation models (LLaVA, GPT-4V-class architectures, Whisper for audio) with as few as 500 to 2,000 labeled examples for many tasks. If you have raw data but no labels, we can stand up a semi-automated labeling workflow using model-assisted annotation to significantly reduce manual effort before human review. We assess your data situation in the first two weeks and adjust the approach accordingly.

Our engineers have production experience with vision-language models (LLaVA, InternVL, PaliGemma, GPT-4o-vision via API), speech-to-text and audio classification (Whisper, wav2vec 2.0), document intelligence (combining OCR with layout-aware transformers like LayoutLMv3), and video understanding (frame-level feature extraction with temporal aggregation). We're framework-agnostic — PyTorch is our default, but we've delivered TensorFlow and JAX workloads for clients with existing infrastructure.

All training runs can be executed entirely in your cloud account or on-premises — we never move sensitive data to Codieshub infrastructure. We use your VPC, your object storage, and your secrets manager. For regulated industries (healthcare, finance), we document data handling in a DPA, apply differential privacy or federated learning where the threat model requires it, and deliver a data lineage report as part of the final handoff.