PAIR Systems × Slalom:
GoodMem, the autopilot memory layer for agentic AI.
GoodMem self-tunes against the customer's own production traces, so accuracy compounds across systems and over time. Agent Tuner adjusts retrieval continuously, without a per-engagement ML project to staff. The agents your clients deploy hold up in production, and cost measurably less to run.
The enablement package.
Story & Pitch
The aviation analogy and the one line every AM can land in a client conversation.
Use Cases
Three lead patterns: BI / text-to-SQL, knowledge base, and ServiceNow workflows.
Partnership
How the deal works. Software is GoodMem, services is Slalom. Pilot motion and rules of engagement.
Clients & Engagements
Production references, integrator bench, and reverse-direction asks.
Sales Tools
Salesforce blurbs by audience, the deck, and the team behind PAIR Systems.
Same agent. Same model. The memory layer is what changes.
And the loop is what makes it compound. Drag the seam to see the world your clients live in today vs. the world GoodMem proposes.
drag the seam to compare
The technician tuning the engine
while the plane is still flying.
Same agent, same model. GoodMem works underneath, on live traffic, so your client's most expensive queries get cheaper and the answers stay just as good. These are benchmark results, not estimates.
The typical query, before and after GoodMem.
One question to a production enterprise BI agent. Same agent, same model. The only thing added is GoodMem's memory layer.
Illustratively, a client spending $100K a month on their agent gives back about $28K a month, roughly $340K a year, from a benchmark-grade 28% token reduction with no change to the agent. Because the savings land on the heavy queries that grow fastest, the benefit compounds as adoption rises.
Source: PAIR Systems benchmark, “Memory That Pays for Itself,” June 2026. Controlled A/B on a production enterprise BI agent across 17 matched scenarios; aggregate token burn down 28% (95% CI 2–47%), up to 66% on the heaviest queries, answer quality unchanged. Pilot-scale; broader validation in progress.
Across the whole bill. On the heaviest queries, up to 66% fewer tokens, with answer quality unchanged. The bill shrinks as memory compounds, instead of growing with every new user.
A/B benchmark · 95% CI 2–47%Frontier-class document understanding that runs entirely on your client's own hardware. Ranks second on public benchmarks, behind only Gemini 3 Pro and ahead of every other model tested.
Public benchmarks · May 2026Java and PostgreSQL, not a Python prototype. SLSA Level 3 supply chain, FIPS cryptography, signed images. ISO 27001 certified; SOC 2 Type II in its final stage.
Security & trust brief · June 2026GoodMem is the autopilot memory layer for agentic AI.
GoodMem sits underneath the agents your clients deploy and self-tunes against the customer's own production traces. Agent Tuner adjusts retrieval continuously against live evaluation traces. Client agents become measurably more accurate and cheaper to run as usage grows, without a per-engagement ML project to staff.
Most enterprise AI projects stall at PoC because the system around the model is hand-rolled. The reflex is to buy a bigger model, and the bigger model doesn't fix it. The model is not what's broken. The system around the model is.
GoodMem replaces that hand-rolled work with software. It is the productized version of what an OpenAI Forward-Deployed Engineer does at roughly $1M per year. PAIR Systems builds and ships the platform; Slalom delivers it.
Slalom Account Managers need a working answer when clients ask for AI.
Every Slalom client is being told to "do AI." Most Account Managers are walking into those conversations with the same playbook every GSI is using: a discovery, a roadmap, a six-month engagement on faith. The agent demos well. It dies in production. The CEO is asking again next quarter.
GoodMem is what Slalom adds to that conversation. A platform delivery teams can plug in on day one. A structured 30 to 90-day pilot with a measurable target the client only signs against if the numbers are there. A motion that gets repeatable across the practice. The point we're building toward: a Slalom motion where you don't even need us to sell it. The license just turns on.
Software is GoodMem. Services is Slalom.
The client contracts with Slalom. GoodMem is a line item on that contract. Slalom marks it up however Slalom wants. Slalom delivers the engagement. GoodMem sits underneath as the autopilot memory layer. Two companies, one motion, one client relationship.
Slalom keeps the relationship and the services revenue. PAIR Systems adds the software margin on top. The client gets a working agent faster than any GSI alone can deliver. Read the commercial detail →
Three things from the Slalom team.
Route the pursuit.
The Salesforce flag is in motion. Next: introduce PAIR to the Account Managers whose books overlap our pipeline so they can begin shaping joint conversations.
Pick the lead client.
Together, choose one client where the lead use case is already on the roadmap. Run a 30 to 90-day pilot with a measurable accuracy target.
Sign an MOU.
Formalize partnership intent and working scope. Joint pipeline review at the next working session.