10decoders01
The pilot has been "nearly done" for two quarters
Every review closes three issues and opens four. The pattern is not scope creep — it is a discovery loop running through a mediator.
Forward Deployed Engineering
Engineers who ship inside your customer's environment.
AI features do not fail in the demo. They fail in production, against data nobody described to you. We put production engineers where that happens — writing code in your repository, on your release train, under your architecture.
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Engineers
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Global clients
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Delivery locations
ISO
27001 / 9001
Trusted by leading enterprises and healthcare teams
When you need one
Six signals that a handoff will not get you there.
Forward deployment is not the right answer to every engineering problem. It is the right answer when the distance between your product and your customer's reality has become the thing holding up revenue.
02
Accuracy is fine in your tests and poor in theirs
The gap sits in document variants, naming conventions, and exceptions your evaluation set never contained.
03
You cannot get the data out to debug it
PHI, PII, or a sovereignty clause means the failing records never leave the customer's tenant. So the engineer has to go in.
04
Each new logo costs more to onboard than the last
Implementation effort is rising with account count instead of falling. That is a product problem being solved as a services problem.
05
Your best engineers are permanently on customer calls
The people who should be building the platform are absorbing account variance, and the roadmap is paying for it.
06
Branches are accumulating per account
Two or three customer-specific forks exist and nobody has scheduled the merge. The cost of that decision compounds quietly.
Definition
A production engineer, not a liaison.
The title gets used loosely. Here is the specific thing we staff, and the specific things it is not — so you can tell whether this is what you are buying.
What a 10decoders FDE does
- Works inside the customer's environment — their tenant, their VPC, their data residency boundary.
- Holds commit access to your repository and ships through your review and release process.
- Instruments the workflow first, so the failure mode is measured before it is argued about.
- Owns a numeric outcome — first-pass accuracy, exception rate, time to first correct output — not a task list.
- Writes the generalisation back into your platform, and says out loud when something should stay bespoke.
- Runs a scheduled handover so your team can operate what was built without us.
What it is not
- Not a business analyst producing requirement documents for someone else to build.
- Not staff augmentation billed against a backlog you maintain.
- Not a pre-sales solutions architect who leaves after the demo.
- Not a support tier that escalates rather than fixes.
- Not a parallel team building a shadow version of your product.
- Not an open-ended residency — every deployment has a defined exit.
How the engagement runs
Land, instrument, ship, transfer.
Four phases, in order, because each one produces the input the next one needs. The sequence is fixed; the duration moves with the environment we are landing into.
Phase 01
Land
Week 1–2
Access, environment, and the boundary. We agree what the engineer can see, where code runs, and which single workflow we are pointed at.
- Access and security review
- One named workflow, one named owner
- Baseline measurement taken
Phase 02
Instrument
Week 2–4
Before changing anything, we make the failure visible. Traces, evaluation sets built from the customer's own records, and an error taxonomy everyone agrees on.
- Trace and log instrumentation
- Evaluation set from real records
- Failure modes ranked by volume
Phase 03
Ship
Week 4 onward
Fixes go into your product through your normal review path. Each change is tied to a measured failure mode, and the evaluation set says whether it worked.
- Changes merged to your mainline
- Regression run on every release
- Weekly movement on the baseline
Phase 04
Transfer
Defined at the start
The deployment ends. Runbooks, evaluation harness, and the decision log go to your team, and we agree what stays in core versus what remains configuration.
- Runbook and on-call handover
- Evaluation harness handed to your CI
- Core-versus-config decisions recorded
Who actually lands
A forward deployment is one engineer at the customer and a small group behind them. The engineer in front is senior enough to make architectural calls without waiting for a weekly steering meeting.
Senior full-stack or applied AI engineer. Sits in the customer's environment and holds the outcome.
Keeps changes mergeable into your core product and blocks anything that would fork it.
Owns retrieval, extraction, and evaluation quality — the part that usually explains the accuracy gap.
Named accountability for scope, security posture, and the weekly number your sponsor sees.
The rule that makes it safe
Customer work has to end up in the product.
Forward deployment fails in one specific way: it produces excellent per-customer software and a platform that quietly stops being one product. We run three standing rules to stop that, and they are written into the engagement, not left to judgement.
Rule 01
Every fix is classified before it is merged
Core, configuration, or genuinely bespoke. A change cannot ship without that label, and the bespoke bucket needs a named reason and an expiry date.
Rule 02
The second customer pays for the generalisation
The first time we see a pattern it is an exception. The second time, it becomes a platform capability with a test — not a copied branch with a different constant.
Rule 03
The evaluation harness outlives the engagement
Whatever we built to prove the fix works stays in your CI. If it leaves with us, you have bought a result rather than a capability.
Where we deploy
Regulated environments, where the data cannot travel.
Our forward deployments concentrate where two things are true at once: the accuracy bar is high, and the records that would explain the failure are not allowed to leave the customer's boundary.
Healthcare · HealthTech
Claims, clinical documents, and interoperability
Extraction and validation break on payer-specific rules and document variants that never appear in a vendor's test corpus.
- Pre-denial claim validation and RCM workflows
- Clinical document extraction under PHI constraints
- HL7 / FHIR interoperability edge cases
- Provider-side deployment inside the customer tenant
Fintech · BFSI
Screening, payments, and compliance evidence
False positive rates are set by local name conventions and transaction patterns, not by the model you picked.
- AML and sanctions screening tuning
- Payment exception and reconciliation flows
- Audit-grade decision logging
- Regulator-facing explainability
AI platform teams
RAG and agentic systems that have to hold in production
Retrieval quality and schema stability are environment-specific. They are diagnosed on the customer's corpus or they are guessed at.
- Knowledge base and retrieval quality work
- Agentic workflow reliability and guardrails
- Schema-constrained output and drift control
- Sovereign and on-premise model serving
Engagement
Start small, on purpose.
Nobody should commit a year of forward deployment to a partner they have not watched work. Every engagement starts with a bounded piece of it, and the fee for that piece comes back to you if you continue.
Track 01
Blueprint
One engineer, one workflow, one question: where is the gap actually coming from?
- Environment and access assessment
- Instrumented baseline on your real records
- Ranked failure taxonomy with effort estimates
- Go / no-go recommendation, including "do not do this"
Track 02 · Most common
Assurance
A forward deployed engineer in the customer's environment, shipping into your product on your release cadence.
- Named engineer with commit access
- Weekly movement against the baseline number
- Core / config / bespoke classification enforced
- Evaluation harness built into your CI
- Defined transfer date from day one
Track 03
Outcome
For platform teams onboarding several customers at once, where the goal is falling implementation cost per account.
- Multiple concurrent deployments
- Shared generalisation backlog into your core
- Onboarding cost tracked per account
- Commercials linked to agreed outcome metrics
The Blueprint fee is credited in full against the first month of an Assurance deployment. If the Blueprint concludes that forward deployment is the wrong answer for you, we will say so in writing and there is nothing further to buy.
Security and governance
An engineer inside your customer's boundary is a security decision.
We are asking for access to environments your customer is contractually responsible for. That is treated as the primary risk of the model, not a procurement formality.
Certified management systems
ISO 27001 for information security and ISO 9001 for quality management, maintained across delivery locations.
Least-privilege access
Scoped, time-bound, named access. No shared credentials, and revocation is part of the transfer checklist.
Data stays in place
Where residency or PHI rules apply, records are not copied out. Diagnosis happens inside the boundary.
Sovereign and on-premise serving
Where the model itself cannot call a hosted API, we deploy and operate within your infrastructure.
Named accountability
A delivery owner is named on the engagement, with escalation to the CTO. Reviews run on a fixed cadence.
Auditable decisions
Every classification, model change, and threshold move is logged, so an audit can reconstruct why the system behaves as it does.
Questions
Forward deployment, answered.
How is this different from staff augmentation?
Staff augmentation supplies engineering hours against a backlog you own and prioritise. A forward deployment starts from a measured outcome — an accuracy rate, an exception volume, a time-to-first-correct-output — and the engineer decides what to build to move it, inside the constraints you set. The commercial difference follows from that: you are buying a number moving, not a seat filled.
Does the engineer sit at our customer's site, or ours?
Usually neither full time. "Forward deployed" refers to the environment, not the postcode: the engineer works inside the customer's systems, on their data, in their workflow. Onsite time is scheduled where it earns its cost — typically landing week and major milestones. We operate from Charlotte, Chennai, Madurai, and Singapore, with overlap hours agreed at the start.
Who owns the code that gets written?
You do. Changes are written in your repository, reviewed by your team, and merged through your release process. There is no separate codebase we retain and no component you have to license from us afterwards. The evaluation harness and runbooks are handed over on the same terms.
What if your engineer concludes the problem is our architecture?
Then that is the finding, and you will get it early rather than at the end. The Blueprint exists partly to surface exactly this — it is cheaper to hear it in week two than after a year of deployment work has been layered on top of a foundation that cannot hold it.
How do you stop this from forking our product?
Every change is classified as core, configuration, or bespoke before it merges, and the bespoke bucket carries a named reason and an expiry date. When a pattern appears at a second customer it gets generalised into the platform with a test rather than copied. The platform engineer on the pod has standing authority to block a change that would create a branch.
Can you work inside an environment we do not control ourselves?
Often yes, and that is a common case — a HealthTech vendor deploying into a provider's tenant, or a fintech operating inside a bank's boundary. It requires a three-way access agreement and a security review with the end customer, which we run as part of the landing phase rather than assuming it is already solved.
