00 / What we offerPractical AI for the enterprise

AI that pays for itself.

789 Labs turns AI from a growing cost and open-ended experiment into a practical business advantage.

Cost
Controlled
Agents
Custom
Data
Private
[ Use Case ] AI spend · selected workloadsJul 2026
−75%AI cost reduction for our client, with required reliability maintained.
PROJECTED 789 LABS ENGAGED WITH 789 LABS −75% M1 M4 M8 M12
Without 789 LabsWith 789 LabsCurve illustrative · figure from case study
~75%AI cost reduction across selected workloads
100%Reliability maintained while cutting cost
0Company data sent to external AI providers
$1M+In company IP kept private
7-figureOpportunities surfaced by custom agents

01 / Practice areas

Three ways AI becomes an advantage, not a line item.

01[ Cost Control ]

Control spend before it scales

We benchmark your real workloads across models, providers, harnesses and tools to find the cost of a successful outcome, not the advertised price of a token. Then we move you onto the system that holds reliability at a fraction of the spend.

Audit your AI spend
02[ Custom Agents ]

Agents that help run the business

We build custom agents that help operate your business and supercharge your workforce, wired into the internal tools your teams already use. Every agent works toward outcomes you can verify, not responses that merely sound right.

Put agents to work
03[ Private AI ]

Private AI that keeps data inside

We deploy private AI that protects company and customer data, with models running on hardware you own, inside your own boundary. Your information advantage compounds without being handed to an external provider.

Plan a private deployment

Across the stack

We work across the AI stack to deliver what your business actually needs. We bring together the right infrastructure, models, software, and automation around your business, not the other way around.

02 / How we work

A clear outcome at every step. Not an open-ended experiment.

Your metric might be cost per task, hours saved, answer accuracy or data sent outside. Whatever it is, we agree on it up front and measure it at every step.

Values shown are examples

  1. 01

    Audit

    Your baseline

    We study your real workloads, data and systems, and agree on the metric that defines success.

    MetricCost per task
    Today$0.84
  2. 02

    Architect

    Your blueprint

    Models, providers, harnesses and tools scored on your real work. The best one is chosen on evidence.

  3. 03

    Build

    Your proof

    Once it’s live, we compare your metric to the starting point from step 01, so you see exactly what changed.

    Cost per task
    Started at $0.84Now $0.21

    −75%

  4. 04

    Operate

    Your edge, maintained

    We trace failures and re-run evaluations whenever models, prices or needs change.

    • New model released→ re-tested → kept
    • Price change→ re-tested → switched
    • New use case→ measured → added

Step 01 starts with a 30-minute call. Book your audit

03 / Featured work

[ Use Case ] · Private AI ·

Building Bastion

Bastion turned fragmented research activity into shared, current knowledge. Researchers could build on parallel efforts, while the C-suite gained a more complete view for high-impact decisions.

From the Bastion case study

A specialized research team was rapidly growing the IP behind its competitive edge, but its findings moved slowly between projects and up to the C-suite. The information that would make AI useful was also the information the company could least afford to expose. Bastion solved both: a private knowledge assistant that runs on-prem on DeepSeek, connects to the team’s own systems, and answers with citations to the documents, messages and code behind them.

Before and after Bastion
MeasureBeforeAfter
Data sent to external AI providersAt risk0
External AI usage feesPer token$0
Company IP kept private—$1M+
Research knowledgeFragmentedShared, current
[ Company perimeter · on-premises ]
Your knowledgeChat · docs · code
Always currentNear-real-time ingestion
Access-awarePermission-aware retrieval
Private modelDeepSeek · on-prem
Trusted answersCited sources
OutsideExternal AI providers

0 bytes cross the boundary

Permission-aware by design

Researchers get answers based only on information they are authorized to access.

An agentic layer

Coordinates data, retrieves evidence and runs recurring tasks, while balancing limited compute between ingestion and live questions.

Evidence-backed

Every response cites the documents, messages and code behind it.

04 / Who we work with

Built for where you are.

We help companies at every stage move from AI ambition to AI advantage, solving what matters now while building the intelligence, infrastructure, and momentum to lead what comes next.

AI-native startups

AI spend climbing with every new customer. We make the unit economics scale.

See the ~75% cost case study

Scaling companies

Teams stretched thin by growth. We build agents that take on real work.

Enterprises

Data that can’t leave the building. We bring AI inside the perimeter.

See the Bastion case study

06 / Contact

Are you paying too much for AI?

Whether it’s spend that keeps climbing, an agent you need in production, or data that can’t leave the building, chat with our team.

We reply from team@789labs.ai.

Email: team@789labs.aiCost control · Custom agents · Private AI