[ Selected work ]

Real AI projects built by our developers

Here is a sample of the LLM, computer vision, MLOps, NLP and product integration work our AI engineers have shipped for clients. We measure it in outcomes, not activity.

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AI projects delivered: 340+AI projects delivered
specializations covered: 6specializations covered
client retention: 97%client retention

AI developers and engineers: 0+
AI projects delivered: 0+
Client retention rate: 0%
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The kind of teams that hire through us

Seed fintech

Series B healthtech

Enterprise SaaS

Growth-stage marketplace

AI-native startup

Public sector

Case files

Six engagements, six specializations

LLM & GenAI · Fintech

Support assistant that answers 72% of tickets

A Series B payments company needed to cut its support load without hurting CSAT. Two of our AI developers built a RAG assistant over their help center and transaction data.

The team indexed three years of support tickets and product docs into a retrieval layer, then connected it to a guarded LLM pipeline that was scoped to answer only from verified sources.

01
Discovery
Audited ticket volume by category and flagged the 40% that were repetitive, answerable questions.
02
Build
Shipped a RAG pipeline over the help center, transaction logs and past resolved tickets.
03
Validation
Ran the assistant in shadow mode against live tickets for two weeks, tuning it for hallucination-free answers.
04
Rollout
Enabled auto-resolution for high-confidence categories, with a human handoff path for the rest.

Within six weeks the assistant was live in production, resolving most routine tickets without any drop in CSAT.

72%

tickets auto-resolved

6 wks

to production

Computer vision · Retail

Shelf-availability tracking across 200 stores

A retail chain replaced manual shelf audits with a vision model trained on in-store camera feeds, flagging out-of-stock items in near real time.

One of our computer vision engineers trained a detection model on labeled shelf imagery from a handful of pilot stores, then built the pipeline to scale it across the full store footprint.

01
Discovery
Reviewed existing camera coverage and manual audit logs to define what "out of stock" needed to mean per category.
02
Build
Labeled shelf imagery from 5 pilot stores and trained a detection model tuned for occlusion and low light.
03
Validation
Benchmarked model output against manual audits across a full week of pilot-store traffic.
04
Rollout
Deployed inference to edge devices across all 200 stores with a central alerting dashboard.

Store teams now get out-of-stock alerts in minutes instead of waiting for the next scheduled walk-through.

94%

detection accuracy

200

stores covered

MLOps · SaaS

Model serving costs cut 60% at 30M predictions/day

A recommendation-driven marketplace was overpaying for GPU inference. Our MLOps architect re-platformed serving with batching, quantization and autoscaling.

The architect profiled the existing serving stack to find where GPU cycles were wasted, then rebuilt the inference path around dynamic batching and quantized models.

01
Discovery
Profiled request patterns and GPU utilization to find where the existing setup was over-provisioned.
02
Build
Re-platformed serving with dynamic batching, model quantization and workload-aware autoscaling.
03
Validation
Load-tested the new stack against production traffic replays to confirm latency held steady.
04
Rollout
Migrated traffic in stages behind a feature flag, monitoring cost and latency at each step.

The platform kept the same prediction latency for end users while cutting its GPU bill by a wide margin.

-60%

serving cost

30M

daily predictions

NLP · Legaltech

Contract clause extraction at 96% precision

A contract-review platform needed to pull obligations and dates out of unstructured legal documents. One of our NLP engineers built and fine-tuned the extraction pipeline.

The engineer combined a fine-tuned extraction model with rule-based post-processing to catch the edge cases legal reviewers cared about most.

01
Discovery
Worked with legal reviewers to define the clause and obligation types worth automating first.
02
Build
Fine-tuned an extraction model on annotated contracts and layered in rule-based post-processing.
03
Validation
Ran the pipeline against a held-out set of contracts scored by the in-house legal review team.
04
Rollout
Integrated the extraction output directly into the review platform's existing clause-flagging screen.

Reviewers now start from pre-flagged clauses and dates instead of reading every contract line by line.

96%

clause precision

faster review

ML engineering · Media

Recommendation engine lifted watch time 22%

A streaming platform's collaborative-filtering model had plateaued. One of our ML engineers redesigned it around a two-tower architecture with real-time signals.

The engineer replaced the aging collaborative-filtering model with a two-tower architecture that could ingest real-time watch signals instead of only nightly batch data.

01
Discovery
Diagnosed why the existing model's recommendations had plateaued despite growing catalog size.
02
Build
Designed a two-tower model architecture wired to real-time watch and skip signals.
03
Validation
Ran an A/B test against the incumbent model across a slice of the user base.
04
Rollout
Ramped the new model to full traffic once the A/B test confirmed the watch-time lift.

The new model kept improving as real-time signals accumulated, instead of plateauing like the old one.

+22%

watch time

4 wks

to ship

Product integration · Healthtech

AI intake copilot embedded in an existing EHR workflow

A healthtech company needed AI features shipped inside a legacy EHR without a rewrite. One of our integration engineers embedded the copilot into their existing stack.

The integration engineer mapped the EHR's existing data model and extension points, then embedded the copilot as a native workflow step instead of a bolt-on app.

01
Discovery
Mapped the legacy EHR's data model and identified where an intake copilot could plug in natively.
02
Build
Built the copilot against the EHR's existing extension points, reusing its auth and patient data layer.
03
Validation
Piloted the copilot with a handful of intake staff, refining prompts against real patient conversations.
04
Rollout
Rolled the copilot out clinic-wide as a step inside the existing intake workflow, not a separate tool.

Staff adopted it without any retraining, since it appeared inside the workflow they already used.

-35%

intake time

workflow rewrites

Vetting

The four stages every AI developer goes through before joining our team

Every developer on our team goes through the same process before they join us. Fewer than 4 in 100 candidates make it through.

01
Portfolio & production-history review
We look at systems a developer has actually shipped, not just a resume or a list of frameworks.
02
Live ML system-design interview
A senior AI engineer talks through architecture decisions and tradeoffs on a real-world scenario, live.
03
Hands-on build assignment
A scoped build task, scored by senior reviewers against production-quality criteria, not puzzles.
04
Communication assessment
Can they explain a tradeoff clearly and work async with a team they have never met? We check this too.

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