SPR2 weeks · fixed fee
Discovery sprint
Rank use cases, audit the data, model the cost and write the architecture. You leave with a plan whether or not you build with us.
LLM.co · Custom AI Development for Private AICustom AI development
Agents, knowledge search, fine-tuned models and integrations built around one real workflow in your business, measured against an evaluation set, and delivered as private AI you own.
FIG. 02 — IBM 7094, NASA Ames, 1966LLM.co
Services
Four ways we build.
Each one ships with an evaluation set, an audit trail and a deployment you control.
What we build
Six kinds of system. All of them yours.
You own the code, the prompts, the evaluation sets and any weights we train. No platform fee, no per-seat license, no lock-in to us.
AGTAgents
AI agents & automation
Multi-step agents that read, decide and act inside your systems, with a human approval step wherever you want one.
e.g. AP agent matches invoices to POs in the ERP and flags exceptions
RAGSearch
Knowledge & search
Ask questions across contracts, SOPs, tickets and drives. Every answer cites the source page and respects document permissions.
e.g. a policy library searchable by plain-English question
APPApps
Custom LLM applications
Purpose-built copilots and internal tools with the UI, permissions and data connections one team actually needs.
e.g. underwriting copilot that drafts the memo from the file
FTModels
Fine-tuning & training
Open-weight models adapted to your terminology, formats and tone, then distilled to run on smaller hardware.
e.g. a small model tuned on years of engineering change orders
DOCVision
Document & vision AI
Extraction, classification and checking for scans, handwriting, forms, drawings and photos, with confidence scores per field.
e.g. intake packets to structured records
INTSystems
AI integrations
Connectors into the systems of record you already run, so AI output lands where work happens instead of in a chat window.
e.g. NetSuite · Salesforce · Epic · iManage · SharePoint · Snowflake
What custom AI development means at LLM.co
Custom AI development is the work of building an AI system around one specific workflow in your business. The system reads your documents, follows your rules and writes into the tools your staff already use. Off-the-shelf assistants answer general questions. A custom system does a defined job, and it is measured on how well it does that job.
LLM.co builds every system on open-weight models such as Llama, Qwen, Mistral and gpt-oss. That choice lets the finished system run as private AI on hardware you own, in your own cloud account or on an air-gapped network. Prompts, documents, logs and any trained weights stay inside your boundary.
When to build custom AI instead of buying a SaaS tool
Buy a packaged product when the job is generic and the data is low risk. Meeting notes and general writing help are good examples. Custom AI development makes sense when one or more of these is true.
- The work depends on your own records, formats or terminology.
- The output has to land in a system of record such as an ERP, CRM, EHR or DMS.
- Your data cannot leave your network under contract, regulation or policy.
- Usage is broad and steady enough that per-seat or per-token pricing keeps climbing.
- You need to explain to an auditor exactly what the system did and why.
What good enterprise AI looks like in production
A working demo proves very little. A production enterprise AI system has an evaluation set built from real past cases, so quality is a number you can track. It signs users in through your identity provider and applies the same role-based access your staff already have. It logs every prompt, retrieval, tool call and output to your SIEM.
It also has limits. Each action type has an approval rule, a rate limit and a kill switch. When the model is unsure, it says so and routes the case to a person. These controls are built during the Harden phase, before the system reaches hundreds of users.
What drives the cost of a custom AI project
Scope drives cost more than model choice. The main factors are the number of systems the AI must read from and write to, the condition of your source data, the approval and audit requirements, and where the system will run. Scanned paper, missing APIs and strict review cycles add work. A clean data source with a modern API removes it.
Infrastructure is a separate line. Dedicated GPUs in your cloud account cost nothing upfront and scale with use. On-premises hardware is a one-time purchase that usually wins once usage is steady. We model both during the discovery sprint and quote the build one phase at a time.
How to evaluate an AI development company
Ask any vendor the same short list of questions. Their answers will tell you whether the system will survive a security review.
- Will we own the source code, prompts, evaluation sets and any trained weights?
- Can the system run on our infrastructure with no calls to an outside model API?
- How do you measure quality, and will we see the scores before each go / no-go?
- How are permissions, audit logging and human approval handled?
- What happens if we stop working with you after any phase?
What you receive at handover
Every LLM.co engagement ends with the system in your repositories and running on your infrastructure. You receive the source code, prompts, evaluation sets and reports, infrastructure code, runbooks and any fine-tuned weights. There is no platform license and no per-seat fee. Your team can run it, or we can operate it under a support agreement.
Engagements
Start small. Keep everything.
Every engagement ends with code, documentation and models in your repositories.
BLD8–12 weeks
Prototype to production
Phased build with an evaluation set, a go / no-go at each phase and a system deployed on your infrastructure at the end.
EMBOngoing
Embedded AI team
Our engineers working inside your roadmap, shipping a series of AI systems against one shared platform.
AUDFixed fee
AI audit & rescue
A review of an existing pilot or vendor system: what works, what is risky, and what it would take to run it privately.
Reel 02 · SAGE air-defense console, 1956Built to a written spec, then run by the people who own it.
Process
From scoping call to running in your rack.
Fixed-scope phases with a go / no-go at the end of each one. You can stop after any phase and keep everything built so far. How we work
012 wks
Scope
Pick the workflow worth automating and prove the numbers.
- Use-case ranking
- Data audit
- Cost model
023–4 wks
Prototype
A working build on your real data, scored against an eval set.
- Eval set v1
- Model bake-off
- Pilot UI
034–6 wks
Harden
Make it safe to give to hundreds of people and an auditor.
- Red-team
- SSO + RBAC
- Audit logging
041–2 wks
Deploy
Installed on your hardware or in your cloud account.
- GPU sizing
- Runbooks
- Handover
05Ongoing
Operate
Monitoring, model upgrades and retraining as the work changes.
- Drift alerts
- Model swaps
- Quarterly evals
Questions
Asked on every scoping call.
What does custom AI development include?
Discovery, design, the build, an evaluation set to measure quality, security hardening, deployment on your infrastructure and handover. You receive the source code, prompts, evaluation sets, infrastructure code, runbooks and any trained weights. Each phase ends with a go / no-go decision, and you keep everything built so far.
How long does a custom AI project take?
The discovery sprint takes two weeks. A focused first system usually reaches production in eight to twelve weeks. The build runs in phases: Prototype in three to four weeks, Harden in four to six, and Deploy in one to two. Integrations and internal review cycles are the main factors in where a project lands.
How much does custom AI development cost?
The discovery sprint is a fixed fee. After discovery we quote the build one phase at a time, so you never sign for more than the next step. Cost depends on the number of integrations, the state of your data, audit and approval requirements, and whether the system runs in your cloud account or on your own hardware.
Do you build on public AI APIs?
Only if you ask us to. By default we build on open-weight models, so the finished system can run as private AI on infrastructure you control. Nothing calls a third-party model API unless you decide it may. That keeps your prompts and documents inside your boundary and avoids lock-in to one model vendor.
Are open-weight models good enough for enterprise work?
For most business work, yes. Extraction, search, summarization, drafting and agent tasks on your own data run well on current open-weight models. We run a bake-off against your evaluation set in the Prototype phase, so you see the scores for each candidate model before you commit to hardware.
Is a custom AI system secure enough for regulated data?
It is built to fit your existing controls. Users sign in through your identity provider, permissions mirror your source systems, data stays on infrastructure you control, and every action is logged to your SIEM. We document data flows so the system can support your SOC 2, HIPAA, GLBA or CMMC program.
Do we need our own AI team to run it?
No. We deliver runbooks, alerts and training so your IT or platform team can operate the system. If you would rather not, we can run monitoring, model upgrades and quarterly evaluations under a support agreement. Either way, the code, models and data remain yours.
What is the difference between custom AI development and private AI?
Custom AI development is the software: agents, search, fine-tuned models and integrations built for your workflow. Private AI is where that software runs: on-premises, in your own cloud account or air-gapped. LLM.co does both, so the system that passes the demo is the same one that passes your security review.
Start here
Submit a job card.
Tell us the workflow and where the data lives. An engineer, not a salesperson, replies within one business day with a first take on architecture and cost.