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Custom AI development services — production LLM features
We build AI features your users actually touch: structured outputs, safe tool access, tracing, and budgets — scoped so stakeholders know what ships and what it costs.
What “AI development” means when you need it in production
Search traffic around AI development often mixes demos with shipping. We treat AI development as product engineering: prompts alone, retrieval over your documents, or agentic flows each get acceptance tests, staging routes, and observability. Before we commit, we align on whether the job is “model API behind a button” or a longer-running agent — because timelines and risk differ. Content on this page mirrors how we explain scope to technical and non-technical buyers, similar in depth to established AI service pages in the industry.
- custom AI development services
- AI software development company
- LLM integration developers
- OpenAI API integration agency
- Claude API development
- Gemini AI integration
- AI feature development cost
- production AI vs prototype
- BalochDev AI development
When custom AI development pays off (and when it does not)
If you are comparing vendors, these patterns keep SEO honest and set expectations — we would rather decline than ship the wrong category of project.
Usually works well
- High-volume tasks with light judgment: triage, drafting with review, classification, and summarization.
- Surfacing answers from docs or tickets where citations matter.
- Internal copilots that accelerate staff instead of replacing policy decisions.
Proceed carefully
- Fully replacing licensed professionals without human oversight.
- “AI transformation” with no concrete workflow — scope needs a named task and owner.
- Buying before data access is settled — we pause until we can test on real inputs.
What buyers get on this engagement
Product shipping habits
We integrate AI into your existing app surfaces — not isolated Jupyter-style experiments.
Security-minded defaults
PII boundaries and retention choices are explicit before we touch production data.
Plain-language milestones
Written phases with demos — easier for legal and procurement reviewers.
Provider flexibility
OpenAI, Anthropic, Google, or open weights — matched to your constraints, not our favorite logo.
Phases from brief to handoff
Like our practice hubs and technology stack pages, we keep scope readable: written milestones, demo checkpoints, and assumed budgets before long commits — so procurement and founders stay aligned.
Discovery & scope
Outcomes, data access, channels, and risk — you receive a phased quote with assumed hours, not a vague roadmap.
Vertical slice
One real workflow on real data proves the model choice and UX before a wide build.
Production build
Auth, monitoring, and rollout — depth scales with integrations and compliance.
Handoff & tuning
Runbooks, prompt/config ownership, and optional retainer for drift and model upgrades.
Typical bands before your final quote
| Phase / package | What is included | Typical timeline | Assumed from |
|---|---|---|---|
| Discovery & written plan | Brief workshops, integration map, acceptance criteria, assumed fee table | 1–2 wks | ~$2.5k–$6k |
| MVP AI feature | One shipped workflow: API, UI, staging, basic eval hooks | 4–8 wks | ~$12k–$45k |
| Multi-workflow / agents | Tooling, audits, expanded channels, stronger eval & monitoring | 8–16+ wks | ~$45k–$120k+ |
Assumed bands are typical before unusual integrations, heavy compliance, or bespoke UI — we confirm fees in writing after a short brief. Most engagements are milestone-invoiced in USD.
Often paired services
Typical deliverables in an AI development engagement
Exact outputs depend on your stack — below is what procurement and eng leads usually expect in statements of work.
- Backend routes & feature flags for model calls
- Admin toggles for models, temperature, and limits
- Logging/tracing dashboards or exports
- Unit + integration tests on critical paths
- Staging checklist and rollback notes
- Short Loom or written handoff for your team
What shipping looks like
Questions people ask before signing
For case studies, see the portfolio — and the parent AI & Intelligence hub.