MAS, OASIS+ and the GWACs determine how quickly you can award — not whether the agency ends up owning what it paid for
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
Last updated:
Federal agencies rarely buy AI through an open competition. They buy through pre-competed vehicles — the GSA Multiple Award Schedule, OASIS+, and IT GWACs such as 8(a) STARS III — structuring the purchase as a task order or a Blanket Purchase Agreement.
That choice determines lead time, competition pool, socioeconomic credit, and fee structure. It is a genuine acquisition decision and it is usually made carefully.
What the vehicle does not determine is the thing that decides whether the programme succeeds: what the agency holds when the period of performance ends.
Every vehicle discussed here can carry either arrangement — a services engagement that constructs a platform the contractor retains rights to, or a licence to a platform that already exists with source rights transferring to the agency. The vehicle is silent on that. It is set by the requirement and the clauses, and it is the question most often left to the default.
Confirm which vehicles the agency can order from and whether AI services sit within scope, rather than assuming from prior use.
This distinction drives both the contract type and the rights position, and it is invisible if the requirement is written as a single services statement.
Decide what the agency must be able to operate, modify and inspect independently before the solicitation is drafted.
On-premise, GovCloud, or air-gapped operation eliminates whole categories of offering and belongs in the requirement, not in evaluation.
Vehicle selection is about award timeline, competition pool, and fee. Treating it as the substantive decision is how requirements end up under-specified.
This is the step that determines what the agency owns, and no vehicle supplies it by default. It is also far cheaper to state up front than to negotiate at closeout.
Vehicles carry multiple contract types. The estimability of platform construction and of integration usually differs, and the structure can reflect that.
Evaluation criteria drive proposals. Criteria that reward staffing depth get staffing depth; criteria that reward delivered capability and rights get those instead.
MAS, OASIS+ and the GWACs can all carry either a services engagement or a licence with source rights. The distinction is set by the requirement and the clauses, and it is the question most often left to the default.
A vehicle shortens the path to award. If the awarded work then constructs a platform from scratch, time to capability is unchanged — and that is the number the mission cares about.
Air-gapped or on-premise operation eliminates whole categories of offering. Stating it as a requirement rather than evaluating it as a discriminator avoids proposals that cannot comply.
Criteria weighted toward staffing produce staffing-heavy proposals. Criteria weighted toward delivered capability and transferred rights produce a different field of offerors.
Where recurring ordering is expected, a Blanket Purchase Agreement is an opportunity to fix rights and deployment terms once rather than renegotiating per task order.
Date of first workload serving mission users against award date
Compare delivered rights against the requirement's stated position
Acceptance test performed by government staff
Review the award structure against the portion-level estimability analysis
Consequence: A fast award of an under-specified requirement, which does not shorten time to capability.
Prevention: Spend the effort on the requirement and the rights position; vehicles are the easy part.
Consequence: The agency funds a system it cannot independently modify or redeploy.
Prevention: State the rights position in the requirement and evaluate it as a discriminator.
Consequence: T&M covering work that could have been fixed-price, or fixed-price covering work nobody can estimate.
Prevention: Assess estimability per portion and structure accordingly.
Consequence: Proposals that cannot meet an air-gap or residency requirement consume evaluation time and sometimes win.
Prevention: Put binding deployment constraints in the requirement.
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.