Insurance
40 sec
median claim triage, down from 9 minutes
A 3B model fine-tuned on a decade of adjuster notes, deployed inside their VPC. Two embedded engineers, eight weeks, no data left the building.
Embedded
by Design.
In your codebase by week two.
We embed engineers in your team to design, build, and run custom AI systems on your data — then hand you the keys.
- To First Ship
- WeeksTo First Ship
- Lower Run Cost
- 62%Lower Run Cost
- Yours at Handoff
- 100%Yours at Handoff
The Handoff
SkipForward90%
Your Team10%
Assessment & Discovery
In ProgressStakeholder interviews, use case mapping
Outcomes
Most AI firms hand you outputs.
We hand you outcomes.
We sell results, not hours. Every engagement opens with one measurable outcome, a baseline we take together, and a date. If the number does not move, neither does the invoice.
Legal Operations
94%
clause extraction accuracy at review-grade confidence
An agent workflow with per-clause evals and human escalation, replacing a queue that took three days to clear. Their team now tunes the prompts.
Retail
$1.2M
annual inference spend avoided
We moved their top three workloads off a frontier API and onto a right-sized open model. Same eval scores, a tenth of the cost per task.
How We Work
Forward deployed, then gone.
A small team of engineers deployed into your organization, shipping in your environment on a clock we set together — with the handoff planned from the first week.
Phase 01
Embed
Our engineers sit with your team, map the workflow end to end, and agree on the one number we are here to move.
Phase 02
Prototype
A working system against your real data, in your environment. Narrow scope, honest evals, no demo-ware.
Phase 03
Productionize
Guardrails, regression evals, monitoring, and CI. It runs on a Tuesday morning without anyone watching it.
Phase 04
Transfer
Documentation, pairing, and training until your engineers own the roadmap. We stay on call, not on payroll.
Four phases, no fixed calendar. Each phase is sized to your scope rather than to a template. One well-bounded workflow can run embed to transfer in about three weeks; several systems in a regulated environment take longer. We size the phases with you during Phase 01, and the dates go in the contract before anyone writes code.
Fixed-scope pilot
One outcome, one price, agreed before we start.
Your stack, your cloud
We build in your repos and your accounts from day one.
Handoff in the contract
Exit criteria are written down, not left to good intentions.
Capabilities
What we build for you.
No platform to license and no seats to buy. We build the system your business needs, in your codebase, and leave it running.
Custom agents & workflows
Systems that do the work end to end — tool use, escalation paths, and audit trails your operations team can defend.
Model selection & tuning
Frontier APIs where they win, a tuned open model where cost, latency, or residency demand it. Chosen per workload, not per ideology.
Evaluation & guardrails
Task-specific eval suites, regression gates in CI, and refusal behavior tested before anything reaches a customer.
Data & retrieval pipelines
The unglamorous half: extraction, chunking, permissions-aware retrieval, and freshness you can point a regulator at.
On-prem & VPC deployment
Inference in your cloud account or your data center. Cost, latency, and residency stay under your control.
Team enablement
Pairing, code review, and internal workshops so your engineers ship the second and third system without us.
Model Strategy
The model is an implementation detail.
We start on whatever proves the outcome fastest, which is usually a frontier API. If cost, latency, or residency later justify moving to a smaller open model we fine-tune and you own, we do that work too. Plenty of engagements never need it — and we say so rather than sell it.
Worth right-sizing when
- Inference spend is material at production volume
- Latency has to fit inside a live workflow
- Data residency rules out a third-party API
Not worth it when
- Volume is low enough that API pricing wins
- The task needs frontier-level breadth or reasoning
- Your team would rather not operate GPUs
The Firm
“Most AI programs stall on integration, not intelligence. The model was never the hard part — the workflow around it was.”
SkipForward is a small engineering firm. We work the way the best internal teams do: in your repositories, against your constraints, accountable for a number someone in your organization already reports on.
- Founded
- 2024
- Engagement size
- 2–4 embedded engineers
- Typical first ship
- 6 weeks
Client Perspective
In their words.
SkipForward helped us integrate Claude into our customer support pipeline. Response times dropped 60% and our team now handles 3x the volume.
Sarah Chen
The SkipForward team's deep understanding of both the technical and strategic sides of AI made all the difference. Our fine-tuned model outperforms GPT-4 on our specific use case.
Marcus Webb
We went from idea to production AI agents in 6 weeks. SkipForward's approach is practical, fast, and actually works.
Lisa Park
Start Here
Let’s find your first outcome.
Thirty minutes to walk through the workflow you want to change. You leave with a one-page scope, a measurable target, and a price — whether or not you hire us.