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Build with GPT, optimized to ship.

OpenAI is the AI lab behind ChatGPT, founded in 2015 by Sam Altman, Greg Brockman, Elon Musk, Ilya Sutskever, and others. ChatGPT (the consumer product) and the OpenAI API (the developer platform) are both built on the GPT model family — currently GPT-5.5, GPT-5.4, and GPT-5.4-mini for text + reasoning, gpt-realtime-2 for voice, and GPT Image 2 for images. GetWidget is not affiliated with OpenAI; we're an independent OpenAI development partner that builds production applications on top of GPT for businesses worldwide.

OpenAI consulting is strategy: we audit your existing AI workload (or evaluate a future one), recommend which GPT model fits each use case, project token costs, and give you a 90-day implementation roadmap. We deliver a document, not code. OpenAI development services are build: we ship the actual integration, eval suite, monitoring, and runbook against your real systems. Most clients start with a one-week OpenAI consulting audit ($3K) to scope what's worth building, then move to a development pilot ($10–25K) for the highest-ROI workflow. Some teams already know what they want shipped and skip straight to the pilot — both paths work.

Neither is universally better — it's per workload. OpenAI / ChatGPT wins on voice (Realtime API has no equivalent at Anthropic), image generation (GPT Image 2), ecosystem maturity (most LLM libraries default to OpenAI), and the Assistants API for stateful chat-style apps. Claude wins on long-context (200K vs 128K), tool-use stability on long agent runs, and Constitutional-AI safety posture for regulated industries. For high-volume classification, GPT-5.4-mini and Claude Haiku 4.5 are both excellent and the choice comes down to latency and cost on your specific eval. We're <a href="/services/claude-development/">model-agnostic</a> — we ship both vendors and pick per workflow.

Most pilots ship in 4–6 weeks after a one-week audit. Realistic distribution: simple integrations (CRM enrichment, ticket triage with RAG over a clean docset) in 2–3 weeks. Mid-complexity (Realtime voice agents, multi-system function-calling agents, vision pipelines) in 4–6 weeks. Complex (regulated workflows on Azure OpenAI, multi-model routing, custom evals against historical data) in 6–10 weeks. We don't quote a 30-day timeline for work that takes 90 days — the audit phase tells us which bucket you're in before any contract.

Three engagement tiers. A one-week audit is $3,000 — discovery, system mapping, model recommendation per workflow, token-cost projection, and a 90-day roadmap. A pilot integration is $10,000–$25,000 fixed price, 4–6 weeks — one workflow shipped end-to-end with eval, monitoring, and runbook. A continuous OpenAI team is from $5,000 per month — embedded PM + engineers shipping integrations on your roadmap with monthly cost-of-ownership reporting. Per-workflow run-cost (model calls, vector DB, monitoring) typically lands at $200–$1,500 per workflow per month depending on volume and which GPT tier the workflow uses.

Yes — and the work is usually less painful than teams expect. Most GPT-4 system prompts translate to GPT-5.4 with minor edits to take advantage of better instruction-following and tool-use schema. We rebuild the eval suite first, run shadow-mode against your current pipeline, and only cut over when the data shows quality is equal or better. Typical migration: 4 weeks for a single application. Post-migration we run the token-optimization pass (Batch API + prompt caching + routing 5.4-mini for narrow tasks), which typically cuts effective spend by 30–60% on top of the model-version savings.

Yes. Azure OpenAI Service is our default for HIPAA, SOC 2, PCI, and FedRAMP-bound workloads. You get the same GPT-5.4 / 5.5 models hosted inside Azure's compliance posture, with PrivateLink so prompts never leave your VPC, KMS encryption, CloudTrail-equivalent audit logging, and a BAA on request. For workloads outside Azure's regions we also work with OpenAI Enterprise direct (SOC 2 + DPA + EU residency available) or hybrid open-source deployments (Llama / Mistral on your own infrastructure) for air-gapped or sovereignty-bound requirements.

Four tactics, in order of impact. (1) Model routing — route 70% of decisions to GPT-5.4-mini (~10× cheaper than 5.4) and only escalate to 5.4 or 5.5 when the eval says it's needed. (2) Prompt caching — OpenAI's caching cuts repeated-prefix reads to ~10% of normal input cost; we restructure prompts so system prompt + tool definitions + long context become cacheable. (3) Batch API — for non-realtime workflows (overnight reporting, bulk classification), Batch API gives 50% off all input and output tokens. (4) Context compression — long-running agents bloat their own context; we add summarization layers that compress old turns into short gists. Stacked, these typically bring effective token cost to 8–15% of the naive baseline at the same eval-suite quality. We include this optimization pass in every OpenAI pilot.